Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.

Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).

These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,

No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.

Two critical flaws in Oracle Database Server

The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.

This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.

The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”

Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.

“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.

A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.

CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.

Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.

There were also two critical flaws in Oracle’s TimesTen in-memory database.

The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.

Volume repair

Gogia said the volume itself marks a shift.

“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”

He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”

Third Tuesday, quarterly cycle

The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.

Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.

“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”

Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”

Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.

“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.

Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.

Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.

This article first appeared on CSO.

Oracle is expanding its Cloud@Customer on-premises portfolio with a managed database offering that it says will enable enterprises to run databases, applications, and AI agents in their own data centers, helping CIOs modernize mid-sized workloads while meeting data residency, regulatory, and low-latency requirements.

The hybrid cloud offering, Base Database Cloud@Customer, combines existing database and infrastructure services such as the Base Database Service and Data Infrastructure Cloud@Customer X11 platform. It is designed for enterprises that do not need the scale of Exadata Cloud@Customer but still want their infrastructure and AI capabilities on-premises, managed by Oracle, the company said.

The Cloud@Customer X11 platform itself consists of two Oracle X11 compute servers and shared all-flash storage, offering up to 60 usable processor cores and 660 GB of memory per server, 47.2 TB of storage, and 10/25 GbE networking.

For regulated industries or restricted connectivity

Analysts see the new offering filling a gap for enterprises that want the operational and economic benefits of the cloud but cannot send their data to a public cloud because of legal restrictions or technology limitations.

These enterprises, according to Ashish Chaturvedi, executive research leader at HFS Research, are likely to be in regulated industries such as financial services, healthcare, government, and defense that must comply with data residency requirements, or needing low-latency access from remote sites to operational databases.

The offering could also appeal to enterprises modernizing mid-sized workloads at remote locations or within individual business units that could never justify the investment in a full Exadata rack, said Amit Chandak, chief analytics officer at IT consulting firm Kanerika.

In all cases, Chaturvedi said, the appeal of the offering is its managed nature, which takes away the burden of looking after the underlying infrastructure.

Deployment and maintenance becomes easier too, said Michael Leone, principal analyst at Moor Strategy and Insights: “They get automation that mid-size teams rarely have the staff to build. Clustering, patching, standby databases, and backups arrive configured instead of hand-assembled because the offering is managed.”

The economics are equally compelling, Chaturvedi said. The pay-as-you-go pricing model, combined with online compute scaling, helps enterprises avoid overprovisioning and paying license fees for idle cores, which is a “classic waste” of fixed on-premises systems, he said.

Private AI behind the firewall

Beyond the operational and economic benefits, the architecture of the new offering enables databases, applications, VMs, and AI agents to be collocated on the same platform, removing what Chaturvedi called “the single biggest blocker” to AI adoption in regulated environments: the need to keep private data behind the firewall.

“For a CIO in a regulated sector who wants to deploy AI agents but can’t let regulated data touch an external model API, that’s a real unlock,” Chaturvedi said.

More so because most AI offerings, at least in their present form and state, cannot guarantee sensitive data protection, said Igor Ikonnikov, advisory fellow at Info-Tech Research Group.

Even if Base Database Cloud@Customer turns out more expensive than fully cloud-based options, “It’s still attractive as it eliminates reputational and economic risk caused by possible AI-induced data leakage,” Ikonnikov said.

The offering’s consolidation of databases, applications, and AI agents will also simplify deployment of AI-based workflows, said Forrester principal analyst Noel Yuhanna. “It reduces stack complexity and helps accelerate development cycles, deliver real-time data, and eliminate data movement challenges.”

Despite those advantages, Chandak cautioned that the offering is unlikely to see broad adoption outside Oracle’s existing customer base: “If a company isn’t already on Oracle, the pull is weak. You don’t buy into Oracle’s database just to get this.”

Enterprises seeking similar hybrid cloud capabilities have no shortage of alternatives: AWS, Microsoft, Google Cloud, IBM, Dell Technologies, and HPE all offer combinations of on-premises infrastructure, cloud management, and AI services.

However, those alternatives typically require customers to integrate multiple software and hardware components rather than consume them as a single managed offering.

Oracle’s differentiation, although narrow, is hard to match, Chaturvedi said: “The vertical integration of database, engineered hardware, cloud management, high-availability architecture, and now private AI, all engineered together and delivered as a managed on-prem subscription should be genuinely convenient and attractive.”

The offering is compatible with Oracle AI Database 26ai and Oracle Database 19c in Enterprise Edition and Standard Edition configurations. It also supports Oracle Real Application Clusters, Oracle Data Guard, and Zero Data Loss Recovery Appliance through Oracle-managed cloud automation for high availability and disaster recovery, the company said.

Base Database Cloud@Customer is now generally available, Oracle said. It did not provide pricing.

This article first appeared on CIO.

Separating transactional databases from analytical systems was, until recently, considered good architecture. Now, as enterprises adopt AI agents that continuously read, reason over, and act on business data, data warehouse and database vendors are increasingly deciding that separation has become a liability.

Just weeks after Databricks unveiled its Lakehouse Transaction and Analytical Processing (LTAP) offering based on Neon Postgres to bring operational (OLTP) and analytical (OLAP) processing closer together, EnterpriseDB (EDB) has introduced converged analytics capabilities for its managed EDB Postgres AI database service with the same intent.

Both vendors are responding to the same pressure of enabling AI agents for enterprises to operate on fresh operational data without waiting for pipelines and replicas, but EDB argues its approach starts from a fundamentally different place.

“Databricks is building from the lakehouse outward, trying to pull transactional capability in through Lakebase,” said Max Romanenko, chief engineering officer at EDB, while “we’re building from the operational layer with Postgres, which is where enterprises already run their most critical workloads, and expanding from there.”

In contrast to Databricks’ lakehouse-centric LTAP, EDB keeps Postgres as the operational source of truth and uses Apache Iceberg as a shared catalog layer connecting Postgres with ClickHouse, WarehousePG, and Spark compute engines, Romanenko said.

In this way, operational data remains in Postgres while historical and tiered data is stored in Iceberg-managed object storage, allowing analytical engines to query the same data through a common catalog without requiring separate copies or ETL pipelines, he said.

That architectural distinction matters to EDB, according to Romanenko, because the vendor is targeting enterprises that want AI and analytics capabilities without moving sensitive data into a cloud-managed platform: “For us, it’s always been about the data sitting on infrastructure the customer owns and controls.”

Focus on data sovereignty and predictable economics

EDB’s promotion of control “will resonate with CIOs focusing on sovereignty, regulated data, and hybrid deployment,” said Stephanie Walter, practice leader of AI stack at HyperFrame Research. “This should enable them to run AI and analytics closer to the data, on infrastructure that their enterprise controls, without creating yet another proprietary data estate.”

For Ashish Chaturvedi, leader of executive research at HFS Research, EDB’s approach in converged analytics will offer more predictable costs than Databricks LTAP for CIOs already struggling to manage their analytics and AI budgets.

EDB’s per-core pricing model can make costs easier to forecast than consumption-based cloud data platforms, where query volumes, AI workloads, and data processing demands can cause bills to fluctuate, Chaturvedi said.

But predictable bills are not necessarily lower bills, warned, Igor Ikonnikov, advisory fellow at Info-Tech Research Group. “The hardware requirements for high-speed operational data processing are higher and relatively more expensive compared to cheap lakehouse storage,” he said.

EDB’s architecture could also simplify data governance by reducing the number of platforms enterprises need to manage. Since operational, analytical, and AI workloads can access data through a common Postgres-Iceberg foundation, enterprises may be able to avoid deploying and governing multiple specialized data stores, and so have fewer systems to license and secure, according to Devin Pratt, research director at IDC.

Reducing architectural tax for engineering teams

EDB’s converged analytics could also simplify operations for developers and data engineering teams.

Its architecture reduces the number of systems developers must integrate and maintain, while eliminating much of the pipeline work traditionally required to move data between transactional and analytical systems, according to Walter.

And, said Pratt, “Zero-ETL means far less plumbing to build and break, so engineers spend their time creating value.”

EDB and Databricks are not the only ones pursuing converged analytics to support agentic systems and other applications needing immediate access to operational data, historical context, and governance controls.

Snowflake has been expanding support for operational workloads by embracing open table formats,and Microsoft has combined transactional and analytical services under a broader data architecture via its Fabric platform.

Evolution of autonomous databases?

Converged analytics, though, was only one part of EDB’s update to its Postgres AI platform.

It has also made generally available what it calls an “agentic database” feature, designed to automate routine database administration tasks.

The system continuously monitors hundreds of operational and performance metrics, detects anomalies, recommends corrective actions, and, where enterprise policies permit, can automatically apply fixes, the company said.

These automated agents can help enterprises optimize and tune their databases up to 10 times faster, it said.

Walter remained skeptical: “It is more an evolution of autonomous database concepts than a wholly new category. Oracle and other database vendors have offered autonomous database capabilities for years.” Where EDB can differentiate itself, she said, is in extending those autonomous capabilities with AI-driven reasoning, automated remediation, and governance controls that allow enterprises to determine how much authority the system receives.

Databricks is pitching a fix for what it sees as the growing operations mess in enterprise AI. With the launch of Genie ZeroOps, unveiled at its Data + AI Summit, the company is targeting a problem many data teams know too well: it’s no longer building pipelines and models that hurts, it’s keeping them running.

As data estates sprawl and AI workloads multiply, engineering time is increasingly eaten up by maintenance. Meanwhile, AI coding tools are accelerating development, churning out even more assets that need oversight, widening the gap between how fast teams can build and how much they have to manage.

Databricks Genie ZeroOps is a new agentic operations capability that is designed to automate the monitoring, investigation, and remediation of issues across data and AI workloads.

Currently in private preview, ZeroOps uses an AI agent to identify anomalies, trace root causes using metadata and lineage information via Unity Catalog, generate proposed fixes, and then test those fixes in an isolated environment before pushing them out for human review to be applied in production.

Targeting real operational complexity?

Genie ZeroOps addresses a legitimate enterprise challenge around operational complexity, particularly the growing burden of maintaining data and AI workloads in production, analysts say.

“Most data teams spend more time keeping pipelines and models alive than building new ones,” said Amit Chandak, chief analytics officer at IT consulting firm Kanerika.

Echoing Chandak, independent consultant David Linthicum said enterprises continue to grapple with deployment drift, incident response, compliance checks, and root-cause analysis across increasingly fragmented data and AI estates.

Those challenges, echoed Victor Coimbra, CTO of IT consulting firm Artefact, are compounded by the emergence of agentic coding tools that accelerate the development of assets, such as machine learning pipelines and models that need “babysitting.”

That maintenance burden carries a significant productivity cost, said Robert Kramer, managing partner at KramerERP, noting that activities such as managing infrastructure, deployment environments, support processes, and operational workflows consume time without directly creating business value.

Those productivity drains, according to Coimbra, have proven difficult to eliminate despite the emergence and widespread adoption of automated observability and governance tools.

“What is different here is the agentic piece. Databricks is trying to move from tools that alert humans to systems that diagnose issues, propose fixes, and validate them in a governed environment without breaking anything in production,” echoed Stephanie Walter, practice leader of AI stack at HyperFRAME Research.

Shifting the role of platform teams

That shift, according to analysts, could change the way most enterprise platforms and development teams work currently.

“Skilled engineers spend the majority of their time on toil. If the ZeroOps agent, in the background, handles monitoring, investigation, and fix-proposal, engineers shift from doing the operational work to reviewing it. The traditional split between ‘people who build’ and ‘people who keep things running’ starts to blur,” said Ashish Chaturvedi, leader of executive research at HFS Research.

“Additionally, this would also mean that platform teams (engineers responsible for maintenance) can focus on genuinely novel failures rather than the repetitive ones,” Chaturvedi added.

The shift, according to Coimbra, could also affect how enterprises scale platform teams: “They can stop hiring operations staff in lockstep with every new pipeline. The same team can cover a lot more.”

Given that the capability is still in preview, Kanerika’s Chandak pointed out that the headcount reduction claims may be overstated.

ZeroOps could instead pose the risk of “skill atrophy,” Chandak said.

“If engineers stop debugging because the agent does it, the team’s ability to handle the cases the agent cannot handle becomes a real exposure,” Coimbra added.

What ZeroOps could mean for CIOs

Genie ZeroOps could be attractive to CIOs because it links innovation capacity with operational discipline rather than forcing a tradeoff between the two, Linthicum said.

“The appeal is straightforward: reduce operational drag, shorten deployment cycles, improve service resilience, and enforce governance without scaling headcount at the same rate as workloads,” Linthicum said.

That combination of efficiency and reliability could help CIOs rein in one of the biggest costs associated with operating data and AI environments, Chaturvedi said. “ZeroOps attacks time spent on maintenance. CIOs have watched their data engineering budgets balloon while the proportion of that spend going to net-new value shrinks.”

Linthicum warned that CIOs should consider the new offering with calculated skepticism and seek metrics to validate Databricks’ claims.

“The headline metrics are mean time to detect and mean time to resolve, plus the share of incidents the agent closes without a human stepping in. Those tell you whether it is actually removing the operational complexities that it promises,” Kanerika’s Chandak echoed.

“Underneath these metrics, CIOs should track the accuracy of their root cause calls, the false positive rate on proposed fixes, and the proportion of fixes engineers approve without editing, because that last number is the real trust signal. On cost, they should measure cost per incident handled against the human baseline, net of agent compute,” Chandak added.

That scrutiny, Chandak further added, is even more important for CIOs because Databricks is entering an emerging category.

“Most vendor agent announcements target the build and use layers, helping people write code or ask questions of their data. ZeroOps targets the operate layer, which is less crowded,” Chandak said.

ENDS

First came vector databases, then RAG. Now, the next frontier in enterprise AI is taking shape: context layers that give autonomous agents a shared understanding of the business, a vision Databricks is advancing with Genie Ontology.

Currently in preview, Genie Ontology automatically extracts business context from enterprise data, dashboards, queries, pipelines, documents, and applications and organizes it into a living graph that AI agents can use to understand how an organization operates.

Showcased at the company’s Data + AI Summit, Genie Ontology uses a ranking system inspired by Google’s PageRank to identify the most authoritative business definitions within an organization.

Rather than treating all sources equally, it weighs factors including who created the information, how widely it is used, its links to certified datasets and assets, and how recently it was updated before determining which answer an AI agent should rely on, Databricks CEO Ali Ghodsi said during his keynote late on Tuesday while explaining the new offering.

Organizations can also upload their own business definitions or ontologies to Genie Ontology via Databricks’ existing Unity Catalog Semantics platform, Ghodsi added.

Ontology promises consistency, but readiness remains a hurdle

For CIOs, a unified context layer, such as Genie Ontology, will materially improve consistency, trust, and governance for enterprise AI deployments, according to analysts.

“One definition feeding every agent means you stop getting three different answers to the same question,” said Michael Leone, principal analyst at Moor Insights and Strategy.

“Older approaches, such as RAG and vector search, just pull back whatever looks similar to your question, and they don’t actually understand your business. An ontology gives the agent the meaning a catalog can’t, what your terms mean, and which source to trust,” Leone added.

That improvement in consistency, according to Ashish Chaturvedi, leader of executive research at HFS Research, could also improve trust, which remains one of the most critical barriers to AI adoption.

“The single biggest barrier to enterprise AI adoption is that decision-makers don’t trust AI outputs enough to act on them without checking. An ontology that grounds answers in governed business definitions, with lineage back to source, directly attacks that trust deficit,” Chaturvedi said.

Alternatively, Leone was more cautious about the trust argument: “It’s a promising idea, but it still has to prove itself before I’d lean on it for anything that matters.”

Echoing Leone, HyperFRAME Research’s practice leader of AI stack Stephanie Walter pointed out that ontologies have a missing link, and that is verification: “Ontologies can improve context, but they do not guarantee the answer is correct. An agent can still pull incomplete data, apply the wrong logic, skip rows, misunderstand a workflow, or take the wrong action.”

That verification gap becomes even more critical, according to Leone, because most enterprises don’t have the data and governance readiness required to implement an ontology layer for AI deployments: “If your data and governance aren’t already in order, this just speeds up your existing mess.”

Seconding Leone, Walter pointed out that an ontology cannot fix messy definitions, poor lineage, weak ownership, or fragmented permissions on its own.

Additionally, the analyst pointed out that the hard part for CIOs is not creating an ontology once but keeping it accurate as the business changes: “Enterprises will need clear data ownership, metric ownership, domain expertise, governance processes, and a way to resolve conflicting definitions.”

“Otherwise, the ontology becomes another stale metadata project with a more sophisticated name,” Walter added.

A growing risk of CIO confusion

Beyond data and governance readiness, CIOs also face a growing risk of confusion in the wake of several technology vendors pursuing approaches, similar to Genie Ontology, to ground enterprise AI in a business context, according to analysts.

Over the past year, Snowflake, Microsoft, and others have introduced some form of ontology, semantic, and context-layer offerings, but the problem is in how these offerings are named, Leone said.

“Everyone slapped a different name on basically the same idea. It slows people down as it creates confusion,” Leone noted.

That confusion could also backfire on Databricks and other vendors, according to Bhupendra Chopra, cofounder and CRO of IT consulting firm Kanerika: “While the marketing has converged around context-building offerings, most enterprises will choose the platform where their data already resides.”

HFS Research’s Chaturvedi doubled down on that view, saying CIOs should resist evaluating ontology offerings in isolation and asked them to stick to the mantra of context layer follows data gravity: “If your data lives in Databricks, Genie Ontology is your path. If it’s in Snowflake, Horizon Context is. If you’re a Microsoft shop, the IQ family is.”

Additionally, Chaturvedi urged CIOs to look beyond functionality and assess how open and portable these offerings are, particularly in multi-platform environments where business definitions may need to move across data lakehouses, analytics tools, and AI platforms.

This is where Chaturvedi sees Snowflake differentiating itself from rivals, with its focus on open semantic interoperability aimed at reducing the risk of semantic lock-in as enterprises evolve their data and analytics stacks.

The battle for the AI control plane

Snowflake’s efforts to differentiate itself, though, analysts pointed out, at least for CIOs, draw attention to a larger race among vendors, including Databricks, to become the control plane for enterprise AI.

While Snowflake is attempting to position itself as an AI control layer through a combination of Snowflake Intelligence, Horizon Catalog, and its push for open semantic interoperability, Microsoft is embedding business context and governance across its Copilot, Fabric, and broader AI stack through offerings such as Work IQ, Fabric IQ, and Foundry IQ, Chaturvedi said.

Databricks’ Genie Ontology, too, is part of a similar strategy, Chaturvedi pointed out, urging CIOs to view the offering in the context of the company’s wider effort to position its lakehouse platform as the foundation on which enterprise AI agents are built, governed, and eventually deployed.

“It’s absolutely a control-plane play. When you connect the dots across everything Databricks has announced at this summit, including LTAP, OpenSharing, and Genie Ontology, you see a single place where enterprise data, governance, business semantics, and agent execution all converge,” Chaturvedi added.

Further, the analyst noted that the control-plane strategy reflects Ghodsi’s broader vision that data platforms could evolve into what the CEO describes as an “agentic system of record” — an authoritative source that AI agents read from, reason over, and act through.

The concept mirrors earlier platform shifts, Chaturvedi said, where ERP systems became the system of record for business transactions and data warehouses became the system of record for analytics.

The next battle, the analyst said, is over which platform becomes the system of record for enterprise AI agents.

Moor Insights and Strategy’s Leone agreed that data platforms are well-positioned to compete for that role because they already own the data, governance controls, lineage, and permissions that agents require to operate safely at scale.

Still, analysts cautioned that context alone will not determine which vendor comes out on top.

“The next enterprise AI battleground is not just context. It is verifiable execution,” Walter said.

As enterprises rush to build AI agents that can reason over business data and take action, Databricks argues that the long-standing practice of separating operational and analytical data systems is turning into a liability.

That separation, the cloud-based data warehouse provider says, is becoming increasingly strained as AI agents require simultaneous access to live operational data and historical context to make decisions and take actions in real time, unlike humans, who traditionally can work with data that is minutes or hours old.

At its annual Data + AI Summit, the data warehouse provider introduced Lake Transactional and Analytical Processing (LTAP), a new architecture designed to unify transactional and analytical data on a single storage layer.

The new approach, according to Databricks, differs from traditional online transaction processing (OLTP) and online analytical processing (OLAP) architectures, which typically store operational and analytical data in separate systems.

Traditionally, OLTP databases are optimized for running day-to-day business operations such as order processing, payments, and inventory updates, while OLAP systems are designed for large-scale analytical queries and reporting.

As a result, enterprises often need to rely on ETL pipelines, data replication, and separate infrastructure to move information between the two environments.

LTAP, Databricks said, seeks to eliminate the reliance on ETL pipelines, replicated databases or separate data copies by storing data once in a shared lakehouse layer while allowing dedicated compute engines to handle transactional and analytical workloads independently.

This approach, the company argued, provides AI-driven agents and applications access to both live operational data and historical analytical context without requiring data movement or duplicate copies.

Developer simplicity in the agentic era

Analysts, too, agree with Databricks’ contention that AI agents place new demands on enterprise data architectures.

“Agents don’t behave like people, or even like the apps we built for people. They read for context, loop, try things, then write something back, thousands of times over in ways you can’t fully predict. At that volume, the constant bouncing between production and analytics systems starts becoming the bottleneck. The pressure to collapse that gap is real, and LTAP is one way to approach it,” said Michael Leone, principal analyst at Moor Insights and Strategy.

Bhupendra Chopra, cofounder and CRO at IT consulting firm Kanerika, pointed out that an autonomous agent’s data access pattern makes the traditional architectures brittle: “We’re seeing this directly with clients deploying multi-agent systems, the pipeline layer becomes the ceiling almost immediately as an agent runs hundreds of times per task.”

The analysts also pointed out that the ability to collapse the gap between OLAP and OLTP is likely to help developers design more robust AI agents or applications that enterprises are currently targeting to deploy.

“The most interesting workflow or application patterns are real-time, context-aware applications that combine transactions, analytics, and AI in one flow,” said Stephanie Walter, practice leader of AI stack at HyperFRAME Research.

“Examples include AI agents that update customer workflows while seeing historical account context and fraud systems that act on live transactions and long-term behavioral patterns,” Walter added.

Designing such applications today, however, according to Leone, would require developers to pull together data from transactional systems, data warehouses, vector databases, and other sources through custom integrations, creating significant engineering complexity and maintenance overhead.

Operational simplicity and governance gains for CIOs

For CIOs,  LTAP’s ability to reduce that engineering complexity, according to Ashish Chaturvedi, leader of executive research at HFS Research, will result in operational simplicity as well as cost savings.

“Most prominent advantage would be fewer data pipelines and everything that cascades from eliminating them. Most enterprises don’t realize how much of their data engineering budget is pure plumbing maintenance,” Chaturvedi said.

Kanerika’s Chopra pointed out that a substantial portion of data engineering capacity in mid-to-large enterprises today is consumed by maintaining synchronization between transactional and analytical systems.

The implications, however, Chaturvedi noted, are not limited to developer productivity, architectural simplicity, or cost savings: “The strategic prize is simplified governance. When you have one copy of data under one governance model instead of the same data scattered across operational stores, replicas, warehouses, and vector databases, you’ve solved the governance fragmentation problem.”

That simplification, according to Chopra, will matter operationally for enterprises deploying multiple AI agents, as these workflows can amplify governance gaps at a speed and scale that no human workflow ever did.

LTAP versus HTAP

Despite all its benefits, though, LTAP isn’t the first effort to unify operational and analytical workloads under a single architecture and for years.

The industry has pursued a similar goal through Hybrid Transactional and Analytical Processing (HTAP) architecture, which sought to combine operational and analytical workloads on tightly coupled infrastructure to serve both workload types from the same system.

LTAP, in contrast, separates storage from compute, allowing different engines to access a common data layer while remaining independently scalable, Databricks said.

That separation of compute engines is why analysts think that LTAP might be a better bet than HTAP.

“HTAP never took off because asking one tightly bound system to be great at transactions and great at analytics usually left it mediocre at both, so customers ended up paying a premium for that compromise,” Leone said.

“I think separating storage from compute is the right instinct, and it’s the same move that made the modern cloud data world work in the first place. It matters because the thing that sank HTAP was one workload starving the other, and giving each side its own dedicated engine is exactly how you keep that from happening,” Leone added.

Another reason for HTAP’s failure, according to David Menninger, executive director of software research at ISG, was its requirement for enterprises to replace existing data platforms with a new architecture.

LTAP, by contrast, builds on the now-common practice of separating compute and storage, making the addition of an operational layer less of an architectural transformation and potentially lowering the barrier to adoption, Menninger added.

Not yet the default architecture for AI agents

However, despite the enthusiasm around LTAP, analysts warned CIOs against viewing this as the inevitable successor to existing data architectures.

“CIOs will still need to choose their data architecture based on latency, reliability, ecosystem fit, cost, compliance, and developer experience,” Walter said.

Echoing Walter, Chaturvedi pointed out that for LTAP to become the de facto standard for the industry, Databricks will need more than architectural elegance: “The architecture looks sound on paper. The proof will be in the commit-to-query latency numbers under real load.”

LTAP, Databricks said, is expected to be released soon as part of Lakebase, without providing any specific timelines.

Every organization has data scattered across data warehouses, data lakes, SaaS platforms, cloud drives, and data centers. Data fabrics enable organizations to centralize and control data access, making it easier for users, such as data scientists and citizen data analysts, to find and use trusted and governed data sources. 

Data fabrics, data meshes, and distributed data clouds are all platforms to help IT and data teams put some order to the chaos around the myriad of data sources they support. Large companies need data fabrics due to the volume and variety of their data sources.

“A data fabric can be thought of as the connective tissue that ensures consistent accessibility, availability, and understanding of data across an organization,” says Dominic Wellington, data and AI expert at SnapLogic. “Individual siloed platforms may have their own internal data transfer systems, and particular teams or departments may adopt interchanges that work for that domain, but a data fabric operates at a higher level, ensuring that unified data policies are applied end-to-end across the entire enterprise.”

Types of data fabrics

When reviewing data fabrics, it’s important to consider their primary use cases, supported data types, data processing capabilities, data management structures, and governance functions. Below are some considerations when reviewing data fabrics as features, platforms, and stand-alone products.

• Some data fabrics are optimized for analytics and machine learning use cases and may have limited support for unstructured data.
• Other data fabrics extend the functionality of data governance platforms beyond data cataloging and metadata management and now include persistent data management, data quality, and dataops capabilities.
• Many data integration and API connectivity platforms go beyond proxying, pipelining, and transforming data to include search, governance, and other capabilities from data centralization.
• Some SaaS platforms are extending their connectivity and data integration capabilities, enabling multicloud portability and persistent data.
• The more advanced data fabrics support features needed for AI agents and AI model training. These platforms create a semantic context layer for structured and unstructured data sources, support Model Context Protocol (MCP) integrations, have real-time query capabilities, centralize policy-driven governance, and track data lineage.    

Why data fabrics are needed for AI

Data fabrics are not just for enterprises, and today, even smaller companies need them as part of their AI democratization programs. Here are a few reasons why:

• AI agents in enterprise SaaS solutions need access to broader data sets than those core to their workflows. Platforms such as Adobe, Appian, Oracle, Salesforce, ServiceNow, SAP, and Workday offer data fabric capabilities to bring data outside of the business processes they manage into scope for their AI agents.
• Unstructured data is important for setting the context for AI agents, and data fabrics are now used to provide access to documents, emails, transcripts, and other media formats.
• Data fabrics provide data access standards for the devops teams experimenting with AI code generators, vibe coding tools, and spec-driven development approaches to develop applications and AI agents. 
• As companies use MCP servers to connect AI agents, data fabrics provide a standardized way for the agents to access governed, trusted data sources.

“As AI agents move from generating insights to taking action, the data fabric becomes foundational in the agentic era,” says Irfan Kahn, president and chief product officer of SAP Data & Analytics. “Most enterprises operate across scattered data sources and diverse data landscapes, and what’s needed is shared business context, governed access, and clear accountability for how data is used in decision-making. Without that context, agents can’t fully understand or coordinate across the enterprise to deliver meaningful value.”

Sanjay Koppikar, chief product officer and cofounder of EvoluteIQ, adds, “Multi-agent architectures become untrustworthy when a unifying data fabric architecture is missing, since agents will often work against each other in the service of their own objectives.”

Delivering context to AI agents

AI agents need a combination of real-time data, user information, problem details, and historical context to guide their decision-making. Vishal Sood, president of research and development at Typeface, says, “MCP and data fabrics give agents access, but the harder problem is contextualizing data across multiple sources and ensuring the underlying content, media, and unstructured data are trustworthy.”

Data fabrics are the foundational elements for providing current information and long-term memory to AI agents. They simplify the many-to-many problem of connecting multiple AI models, AI agents, and MCP server integrations to multiple structured and unstructured data sources.

“The data fabric does a beautiful job of encompassing three concepts needed to create applications and processes: the data catalog, the data model, and data access,” says Sanat Joshi, executive vice president of product and innovations at Appian. “But now add business rules, process models, APIs, security groups, the organizational model, and their interrelationships into one unified view of the enterprise, and that becomes your context layer.” 

Integrations with data fabrics

Devops teams just getting started on an AI agent proof of concept may want to connect directly to the optimal data sources and APIs. Michel Tricot, CEO and cofounder at Airbyte, says connecting agents to live APIs is a great start, but it creates two big problems: APIs only return data that an agent already knows to ask for, and every query is an expensive API call chain that, with overhead, can overwhelm infrastructure in production volumes.

Tricot says the data fabric for AI use cases must be dynamic, leveraging discovery of available information from replicated data, fetching live contextual information, and writing the data back to business applications to update records.

Moving data in and out of the data fabric requires an integration strategy. Zero-ETL (extract, transform, load) is one low-cost, efficient approach for connecting to structured data sourced without replicating information. Once information is accessed centrally, it also enables streamlined security and governance.

“The promise of AI agents breaks down when they’re stuck waiting on brittle ETL, dealing with poor data quality, and lacking the right context to perform analysis,” says Preston Wood, chief security and strategy officer at Databahn. “Generating AI-ready data within a data fabric gives agents real-time access to operational data without the latency and drift that undermine decision quality. A well-architected data fabric provides the governance and lineage controls that let you deploy agents confidently, knowing exactly what data they’re touching and why.”

Centralizing AI-ready data

Data fabrics centralize AI-ready data and help data governance teams address data quality issues, biased data concerns, privacy compliance, and other data governance non-negotiables. Data fabrics also help address integration issues, monitor for data pipeline errors, and report on performance latencies. The result is that AI agents, models, and other analytics capabilities can then connect to trusted data sources with consistency.

“As AI agents and MCP architectures increasingly rely on data fabrics as their golden source of truth, data quality stops being a hygiene problem and becomes a trust problem, as we all know that trust is foundational to autonomous decision-making,” says Kellyn Gorman, database and AI advocate and engineer at Redgate Software. “Organizations that invest now in semantic consistency, lineage tracking, and observable data contracts across data fabrics will be the ones whose AI agents can be trusted to act without constant human correction.”

Data fabrics that support zero-ETL and other bidirectional integrations with sources thus become an organizational knowledge base, the data source for training AI models, and a foundation for producing data metrics.

“AI agents are only as reliable as the data they’re built on, and most organizations underestimate how much implicit tribal knowledge lives in their transformation logic rather than their source systems,” says Tobias Ostwald, director of analytics at NMI. “If you’re exposing a data fabric to agents or MCP integrations, you need lineage, testing, and metric definitions baked into the layer itself, not just documented somewhere, because the agent can’t call a colleague to gut-check a number.”

Streamlining security and governance

With a data fabric in place, governance, security, and other risk management leaders have a central location to manage data security, centralize access controls, and fulfill other governance responsibilities. Miles Ward, CTO of AI in Solution Lines at Insight, says, “We have to move past security by isolation to a governance model where the fabric itself enforces the pavement and walls of compliance.”

The data fabric also governs entitlements for AI agents and their users. Centralizing these business rules can help organizations avoid creating AI debt, a risk if controls are implemented directly in data sources or consumers.

“The convergence of AI-generated code sprawl and autonomous MCP connectivity creates a ‘perfect storm’ of architectural drift and toxic permission combinations,” says Karen Cohen, vice president of product at Apiiro. “Effective governance requires a security data fabric that monitors these autonomous connections in real time to enforce intent-based policies and strictly limit agent scope to its specific purpose. By integrating guardrails that align AI-assisted development with secure architecture principles, enterprises can proactively secure their expanding attack surface without sacrificing developer velocity.”

Future considerations for data fabrics

Expect vendors to expand the scope of their data fabrics beyond text and documents. Some will include specialized document processing for common formats such as invoices, contracts, and product documentation. There will be skills and tools to support industry-specific documents such as health records and construction documents. Others will support multimedia file types and provide metadata extraction and search capabilities. 

“Enterprises are asking agents to reason across contracts, images, PDFs, and video, and this is where most data fabrics break,” says Dave Shuman, chief data officer at Precisely. “Multimodal data must be chunked, embedded, and governed with the same rigor as structured data, including lineage and access controls.”

Several other emerging capabilities include:

• Extended support for AI agent interfaces to aid in data discovery, and with greater contextual controls on where and when AI agents can access sensitive data
• Business ontologies, semantic layers, and knowledge graph capabilities, with management tools or integrations with third-party platforms
• Support for data contracts, service-level agreements, centralized data observability, auditing, and other functions that will enhance explainable AI capabilities
• Finops functions to track costs for data owners and consumers

As more companies depend on AI agents in their operations, expect top data fabric platforms to release capabilities to expand scope, scale, use cases, and governance.  

AIs struggle to understand documents designed for humans; the DocLang working group seeks to flip that imbalance with its specification for machine-readable business documents “built from the ground up for LLM tokenizers.”

The working group, founded by IBM, Nvidia, and Red Hat and hosted by the Linux Foundation’s LF AI & Data project, aims to create an open, universal, AI-native document format designed to improve how enterprises prepare, exchange, and govern document data for AI systems. ABBYY and Human Signal will also be involved in its development, and other contributors are welcome.

“Enterprises today work across a fragmented landscape of document formats, including PDFs, JPEGs, and other file types built primarily for human consumption rather than AI interpretation,” the group said in its launch announcement.

“This disconnect can introduce complexity, raise costs, and reduce reliability when extracting meaning from business documents,” as organizations increasingly rely on generative AI and agentic systems, it said.

Mark Collier, executive director of LF AI & Data, said the goal of the DocLang Specification Working Group is to “develop a vendor-neutral, interoperable standard that helps organizations prepare document data for AI more reliably, transparently, and at scale.”

DocLang defines a structured, machine-readable format for documents of any type, like JSON for data, that any tool can implement and any pipeline can consume. It builds on DocLing, a document processing toolkit hosted by LF AI & Data that can transform human-readable PDFs, word processor documents or spreadsheets into structured data.

Standards must evolve for AI

Something like DocLang is needed, said independent technology analyst Carmi Levy. “Existing document standards have done an admirable job allowing global stakeholders to confidently collaborate for decades, but it’s becoming increasingly clear that they are in desperate need of an update as AI reshapes the rules around how work gets done,” he explained.

Largely static document types, he said, “can be somewhat limiting when AI is redefining the very word, ‘document.’ In many ways. AI-age documents are far more iterative and dynamic than what they once were, and the definitions need to evolve with the times. The documents we currently live with simply weren’t designed for the AI age.”

Within that context, Levy said, “DocLang represents an early, best hope of achieving some kind of foundational baseline for document standards, one that will hopefully allow more intelligent, more efficient, lower-risk workflows than is currently the case.”

Taking an open-source, vendor-agnostic approach to the process ensures the collective will take precedence over the needs of specific vendors, he said, adding, “earlier standards-setting efforts around networking, documentation, the web, and the cloud powered the free-flowing digital landscape that defines modern life.”

An AI-centric documentation standard will carry that reality into the next generation of technology, said Levy.

A question of governance

The entire concept of LLMs, Jason Andersen, principal analyst at Moor Insights & Strategy said, “involves using natural human languages. The computer is supposed to understand us without us changing our syntax or language. Forcing a syntax on users is exactly what we have today with SEO and more advanced programming languages.”

With something like DocLang, where the standard can be applied to content ingestion, he said, “I would be OK with that being automated, which seems to be the intent. The use case I envision is that when I upload a document to an agent, a skill can be run to preprocess the document into the DocLang standard format, saving tokens.”

That makes sense, he said, adding that he thinks it’s good “if it can help generate outputs, like a visualization, that can be shared outside an AI tool. On that front, that is also why I am liking Web MCP, since you are just adding some code to the page, like CSS or JavaScript, and the consumer, in this case, an AI browser or skill, is better equipped to handle the site.”

The point, he said, is, “these standards need to preserve the fact that humans can still do what they want, and do not need to know any coding to be proficient. In terms of governance, I am not sure if it matters.”

But one analyst did foresee governance problems arising from DocLang’s use.

Yaz Palanichamy, senior research analyst at Info-Tech Research Group, said DocLang adoption will require organizations to implement and review controls in order to scale its use accountably and securely.

This article first appeared on CIO, on June 10, 2026.

Databricks on Wednesday unveiled OpenSharing, a new open protocol designed to let enterprises share AI models, agent skills, dashboards, and unstructured data across platforms without having to copy or move those assets.

That sharing is made possible by OpenSharing’s zero-copy credential vending model that allows recipients to securely access shared assets directly from a provider’s cloud storage using temporary, scoped credentials rather than requiring the assets themselves to be copied, moved, or replicated, the company wrote on its GitHub page.

Reducing the integration tax of enterprise AI

The ability to share AI assets without creating duplicate copies could help reduce integration complexity, improve governance, and limit the operational overhead associated with operationalizing AI systems across environments for CIOs, said Ashish Chaturvedi, leader of executive research at HFS Research.

“Every organization building AI, such as multi-agentic systems, is hitting the same wall, i.e., the model, the skill, and the consumer reside on three different platforms. The integration tax is enormous, and it grows exponentially with every new partner, customer, or internal team,” Chaturvedi said.

Echoing Chaturvedi, The Futurum Group’s lead of the CIO practice, Dion Hinchcliffe, pointed out that the reduction in operational overhead could help CIOs cut down on the hidden costs of integration around AI deployments: “Today, hidden costs include more than just model development. It is the endless packaging, translation, sync, and governance effort required to operationalize AI assets across organizational boundaries.”

From data sharing to AI asset sharing

That cost reduction is becoming even more important because enterprises are beginning to treat AI assets as business assets that need to be shared, said Stephanie Walter, practice lead of the AI stack at HyperFRAME Research.

“Enterprises are quickly realizing that the value is no longer just in the dataset. It is in the governed context, logic, and intelligence built around the dataset. Existing approaches can share datasets well, but they often do not address the broader AI package,” Walter said.

“OpenSharing is directionally aligned with that shift because it extends the sharing model beyond tables and files toward the artifacts that power AI workflows,” Walter added.

For Hinchcliffe, that alignment should work in CIOs’ favor, trying to operationalize AI across their systems: “CIOs increasingly want AI supply chains, not isolated data lakes like before.”

Additionally, Chaturvedi pointed out that the new protocol can help CIOs accelerate the monetization of AI investments.

“For CIOs, the speed at which you can share AI assets across partners, subsidiaries, and customers determines the speed at which you can monetize your AI investments. If sharing an agent skill takes six weeks of integration work, you’ve lost the window. If it takes a protocol call, you’ve turned AI into a distribution business,” he said.

How OpenSharing could simplify AI development

Achieving those benefits, however, will require developers to move AI assets across disparate platforms more efficiently, and analysts pointed out that OpenSharing’s ability to reduce integration complexity could significantly improve productivity.

“Developer productivity depends on reducing platform translation work. Developers do not want to rebuild the same asset for every consuming environment, and enterprises do not want every partner or customer interaction to become a platform migration conversation,” Walter said.

In fact, Chaturvedi sees the new protocol as unique in the industry, in the specific sense that “no other open protocol covers agent skills and AI models as shareable, governed objects”.

Walter, in contrast, sees the openness of the protocol as novel: “What is more interesting is the combination: an open protocol, cross-platform interoperability, Linux Foundation governance, and a broader asset model that extends beyond datasets into AI models, agent skills, dashboards, applications, and unstructured data.”

“The novelty is not that Databricks invented sharing, zero-copy access, or marketplace-style distribution. Those capabilities already exist in various forms across the market,” Walter said, pointing towards Snowflake’s offerings, such as the Zero-Copy integrations.

The difference, though, the analyst noted, is that Snowflake allows data to be copied only if both provider and receiver are on Snowflake.

With Databricks’ OpenSharing, data can be copied across platforms, Walter added.

OpenSharing, which is an evolution of Databricks’ existing Delta Sharing protocol, is currently a sandbox project under the Linux Foundation AI & Data Foundation and is available via GitHub.

Its current list of generally available connectors includes Python, Apache Spark, Tableau, PowerBI, Snowflake, DuckDB, Clojure, Node.js, Java, Arcurate, Rust, Go, C++, and R.

Other connectors that are expected to be made generally available soon include Google Spreadsheet, Excel, Airflow, and Lakehouse Sharing.

Model Context Protocol (MCP) has gained considerable momentum as a standard connector between LLM-powered tools and local systems, internal and external APIs, and data sources. From major clouds to devops tools, MCP servers are enabling powerful, AI-powered development and operations capabilities through natural language commands.

Nowhere is this more true than in the world of databases. Most major database platforms now support agentic access through MCP servers. Using an MCP server for databases, you and your AI agent proxies can perform lookups, create and update data, and perform administrative tasks without you having to write SQL by hand.

The MCP server could also guide your LLMs to write new code or build automations that align with your database schema, like its tables, structure, and fields, as well as embeddings, indexes, and metadata. It could also aid debugging by enabling faster queries to surface data issues or misconfigurations, along with plenty of other possible use cases.

Below, we’ll cover official MCP servers from some of the top platform options across major database styles. Though maturity varies, the MCP servers discussed below represent some of the best vendor-backed offerings available today across relational SQL, NoSQL, graph, vector, and data warehouse systems.

These servers can be used by any MCP-compatible tool, IDE, or agent, whether it’s Claude Code, Codex, Cursor, Gemini CLI, Google Antigravity, VS Code, Windsurf, or something else. Adding them is typically simple, often involving a lightweight JSON addition to your MCP configuration file.

Amazon Aurora MCP Servers

MySQL and PostgreSQL are the world’s most widely-used open source databases. However, in both cases, there is no canonical MCP server—what we see are different MCP servers emerging across vendors. One of these vendors is Amazon Web Services (AWS), which offers official MCP servers for Amazon Aurora, its managed relational database service compatible with both MySQL and PostgreSQL.

According to the documentation on GitHub, the Amazon Aurora MySQL MCP Server can be used to convert natural language commands into MySQL-compatible SQL queries, which can then be executed against Aurora MySQL databases. Similarly, the Aurora Postgres MCP Server provides MCP tools for working on PostgreSQL databases. The Aurora DSQL MCP Server does the same for distributed Postgres databases.

AWS provides a growing portfolio of official MCP servers across its product line, including MCP servers for other Amazon database platforms like DynamoDB, ElastiCache, and Redshift. If you’re a heavy AWS shop and you want to enable LLM interactions with your data, these are sensible choices.

BigQuery MCP Server

BigQuery is Google’s cloud-based data analytics platform, and a popular data source for AI applications. BigQuery users with API access configured can also utilize the BigQuery MCP Server to interact with the platform using MCP-compatible AI clients.

Using the remote BigQuery MCP Server, engineers can generate and execute queries on data sources, or return metadata on datasets, tables, and schema. This can be done with a simple natural language prompt like “List the datasets in project PROJECT_ID.” Results are filterable by region, data set ID, column name, and more.

As part of Google’s fully-managed, remote-hosted MCP portfolio, the BigQuery MCP Server provides some peace of mind regarding security, maintenance, and ease of use for distributed teams. The MCP tools are subject to some limitations, however, in terms of query result size, processing time, and other factors. If you’re using BigQuery and you want more agentic control, you’ll want to check this one out.

Elastic Agent Builder

Another important database category includes platforms designed for keyword and semantic search. In this space, Elasticsearch is commonly deployed. Instead of providing a single MCP server, Elasticsearch provides the Elastic Agent Builder, which is a more comprehensive framework aimed at agentic workflows.

Using Elastic Agent Builder, you can chat with an agent to retrieve data context from Elasticsearch data and extend it into various environments. The Agent Builder itself includes an MCP server endpoint for programmability and exposing the agent to other clients.

Unlike others on this list, this is not a direct MCP interface to raw Elasticsearch APIs. Instead, it’s an interface that exposes skills from the agent platform. This should also not be confused with the Elasticsearch MCP Server, released in mid-2025, which has since been deprecated.

A possible downside of using this utility is that it includes an additional layer between your IDE or agent and the data you’re searching. The agent setup requires a higher subscription and takes additional steps to configure compared to other MCP servers.

That said, if you want an extensible common layer to interact with both Elasticsearch and external MCP servers, while centralizing responsibilities like permissions, this is an interesting proposition.

MCP servers for Neo4j

Graph databases are another key NoSQL database type these days, specializing in using nodes and edges to accelerate queries of highly interconnected data. Of these, Neo4j is a popular graph database option.

The Official MCP Server for Neo4j works with all kinds of Neo4j deployment (desktop, sandbox, self-managed, and the managed Neo4j Aura cloud service), and allows LLM-based clients to retrieve graph schema, execute read and write statements, execute graph algorithms, and more.

In addition, several other MCP servers for Neo4j are available from Neo4j Labs. These have specialized uses, such as generating Cypher queries from natural language, maintaining an in-memory graph database, modeling and visualizing graph, and interacting with the Neo4j Aura API.

The first MCP server for Neo4j was developed in December 2024. If you’re an avid Neo4j user and want to experiment with interacting with your graph databases in a chat-infused way, these stand as an interesting platform of servers.

MCP Toolbox for Databases

Google’s MCP Toolbox for Databases is a notable MCP server because it’s a popular catch-all for various database types. Unlike the other entries on this list, this server connects LLMs not to a single managed database, but unifies LLM access to multiple systems. The open source utility ships with pre-built configurations for nearly 30 databases including PostgreSQL, MySQL, SQL Server, Oracle Database, MongoDB, Redis, Neo4j, and Snowflake, as well as the databases in Google Cloud.

Once you define data sources in a tools.yaml file, you can use MCP Toolbox to perform structured queries or semantic searches against databases directly from within an IDE or agentic client using plain English. MCP tools translate commands into actions like list_tables and execute_sql.

MCP Toolbox for Databases is mature, originally built as a generative AI utility and later re-worked for MCP-style workflows. It offers numerous download, configuration, and interaction methods.

If you’re using a variety of databases on Google Cloud and elsewhere and you want an “all-in-one” MCP server, MCP Toolbox for Databases is a great place to start.

MongoDB MCP Server

MongoDB is the popular NoSQL document-oriented database. The creators of MongoDB have released an official MCP server that works with the open-source database as well as the company’s cloud-hosted MongoDB Atlas database platform.

The MongoDB MCP Server provides a number of tools to interact with MongoDB. You can query the database, return information on collections, create or remove collections or indexes, gather statistics on database usage, and more. Other tools enable MongoDB Atlas operations, like creating users or clusters, returning cluster data, and other functions.

The server’s tools are read-only by default but can be switched to allow write capabilities. It can be used locally, but also supports Streamable HTTP transport for remote servers, although that comes with greater security concerns.

For those using MongoDB and wanting to hook their AI-enabled IDE or CLI up with more automated powers, the official MongoDB MCP Server is worth checking out.

Pinecone MCP server

Among vector-native databases, Pinecone stands as a strong, widely used option with a well-designed API and comprehensive SDKs. The Pinecone MCP server extends this experience, allowing users to query its documentation and execute functionality via AI agents and AI-enabled IDEs.

To date, the Pinecone MCP server consists of nine MCP tools. These cover read-only actions, like knowledge gathering via the Pinecone official documentation and querying vector records, index metadata, configurations, and statistics. It also allows for write operations like updating records and creating new indexes.

Released in mid-2025, the Pinecone MCP server is one of the more complete early implementations, with easy configuration and installation.

For those using Pinecone who want to test new LLM-assisted workflows for creating indexes with embeddings, performing reranking, or testing results using natural language commands, the Pinecone MCP server is worth trying out.

Redis MCP

An ultra-fast in-memory database, Redis is commonly used for caching, real-time analytics, and other latency-sensitive use cases. And as you might have guessed, the company behind the Redis database provides an official MCP server. The server allows read, query, and write capabilities.

Developers can use Redis MCP from an LLM client to perform high-level actions to analyze, reference, or embed Redis data and interact with the Redis server within their prompts. The documentation suggests some example prompts for common use cases, such as “Cache this item,” “How many keys does my database have?,” and “What is user:1’s email?”

Unlike other MCP servers, which only allow a slice of platform capabilities, Redis MCP offers full Redis support. This enables working with Redis constructs such as hashes, lists, sets, sorted sets, streams, and more, according to the GitHub repository.

One possible drawback is that Redis MCP has yet to support Streamable HTTP transport. Until this is developed, the server is constrained to local deployment. But for those seeking a local MCP server to work with Redis data, this is the best choice.

Snowflake MCP Server

Snowflake is a cloud-hosted, AI-enabled data platform widely used in enterprise contexts for data warehousing, data analytics, and data engineering purposes. Compared to other data storage systems, Snowflake is unique in that it’s more fully managed and combines structured and non-structured data types.

The Snowflake MCP Server, available on GitHub, can be used to perform many of the standard Snowflake platform operations. This includes a “fuzzy” search of all records via Snowflake’s Cortex Search and structured data semantic lookups using Cortex Analyst.

Other abilities include object management operations like creating, updating, and deleting records. The server also can invoke other agentic-designed capabilities, like the ability to generate and execute SQL statements against back-end databases.

Snowflake MCP Server is well-thought-out and well-documented, with walkthroughs for various agent and deployment patterns. Those already building with Snowflake should find it complements the mechanics they already employ.

Supabase MCP Server

A longtime open-source favorite, PostgreSQL is one of the most popular and trusted object-relational SQL-based database systems. With an active open source community, Postgres has been maturing for decades. Given its open source nature, there isn’t a single “official” MCP server for the platform. Anthropic built an original reference implementation, but it’s now archived.

Instead, database platforms built on PostgreSQL provide different flavors of MCP servers, with a range of vendor neutrality and specificity. One notable option is the Supabase MCP Server, provided by Supabase, a cloud-based “back end as a service” and Postgres development platform.

Supabase MCP Server connects AI agents with Supabase projects, allowing engineers to issue natural language commands to manage tables, query data, get logs, fetch configuration information, and more. The Supabase MCP Server is pre-1.0 release and some features are still experimental.

If you’re an engineer using Supabase and looking for an MCP server to connect your AI assistant with your Postgres databases, this is a good tool to test out.

Other MCP servers for databases to consider

So far, we’ve reviewed official, vendor-backed MCP servers from some of the most-adopted managed databases. However, numerous MCP servers exist across other database platforms and types.

One MCP server that aggregates LLM access across various database types is DBHub, which works with MySQL, PostgreSQL, SQL Server, MariaDB, and SQLite. Developed by Bytebase, DBHub is described as a zero-dependency, token-efficient MCP server.

For SQL, the options are nearly endless. Official servers exist for Microsoft Azure SQL and DuckDB. PulseMCP catalogs more than 100 MCP servers for MySQL, although most are unofficial, solo-creator open source projects. Of these, one of the most starred is MCP Server for MySQL, developed by full-stack developer Ben Borla and optimized for Claude Code.

For Postgres, notable alternatives to Supabase include pgEdge Postgres MCP, Neon MCP server, and Postgres MCP Pro. For vector databases, others beyond Pinecone have been quick to adopt MCP as well, including Weaviate and Milvus.

Using MCP for databases: what to watch out for

Before diving into MCP servers for enterprise databases, it’s important to understand the security risks. For instance, prompt injection remains an unsolved problem, so it’s recommended to limit permissions for SQL statements.

To mitigate this, Supabase recommends enabling AI client settings that require manual approval for each tool call before execution. Experts also recommend assigning only the minimum permissions required and avoiding exposure of sensitive data like API credentials. Due diligence around authentication and authorization is especially important when hosting remote servers.

Lastly, to avoid shadow IT, it’s becoming common practice to catalog the internal MCP servers you use, even for experimental projects. For this, experts recommend an MCP registry that documents approved servers. An MCP registry improves both MCP server discovery and security awareness.

Licensing can be complicated, particularly when enterprises are forced to double-pay because the software they already own is only licensed for a specific environment, and moving it requires a whole different licensing model. Without proper portability rights, they need to make additional financial investments to run the same workloads in a different home.

AWS says its new Bring Your Own Media (BYOM) service eliminates this duplication. Customers can now reuse their existing Microsoft SQL Server media and licenses on Amazon Relational Database Service (RDS) with no additional licensing fees.

This means enterprises no longer have to justify workload migrations to the cloud against existing licensing commitments and infrastructure investments, AWS said.

“What used to be a licensing barrier between operational SQL Server data and agentic AI is now gone,” AWS data engineer Srikanth Katakam and product marketing manager Colleen Betik wrote in a blog post announcing the service.

Avoiding the double-licensing problem

To drive true value, agentic AI apps must have access to elastic GPU capacity as well as direct, low-latency access to the data they need to reason and make decisions. But, AWS argued, that can be difficult in self-managed data centers and on premises infrastructure with limited access to agentic AI cloud-native services. But a lot of enterprise data lives in Microsoft SQL Server, and until now customers have had to pay for a second license to use a fully managed cloud service like RDS.

Now, with BYOM on Amazon RDS for SQL Server, enterprises can reuse their existing SQL Server Enterprise Edition or Standard Edition licenses through a “lift-and-shift” model. This brings their data into a fully-managed environment.

According to the licensing distribution terms, enterprises must provide their licensed SQL Server Release to Manufacturing (RTM) media to Amazon RDS. They can then upload that media to Amazon S3 and launch BYOM instances. Enterprises must configure AWS License Manager to perform automatic instance tracking.

From there, RDS automates patching, backups, high availability, and monitoring, while providing direct access to agentic AI and analytics services native to AWS. The platform also reports vCPU usage to provide visibility into SQL Server license usage, Katakam and Betik wrote.

“You do not need to choose between protecting your SQL Server licensing investments and giving your data a path to the AWS analytics and agentic AI services redefining what’s possible in the cloud,” they said. 

It’s important to note that this service is only available to enterprises with Microsoft Software Assurance (SA), an add-on which supports mobility and rehosting of existing licenses. Enterprises must verify that their SQL Server licenses comply with Microsoft’s licensing agreement, and that they have a SQL Server License (Standard or Enterprise) with SA. They also must submit a License Mobility Verification Form to Microsoft; it, in turn, will notify AWS once verification is complete.

AWS emphasized that enterprises remain responsible for compliance; it does not block operations if license limits are exceeded.

More control over workloads, loosening Microsoft’s grip

The advantage of this service, explained Mike Leone, principal analyst at Moor Insights & Strategy, is that enterprises can get a Microsoft SQL Server workload onto a managed AWS service without rewriting it for Aurora or paying for the license twice. This means they can modernize on their own schedule rather than being forced into rewrites before they’re ready.

“For a lot of shops, that control over timing is worth more than the license saving itself,” Leone said.

This also loosens Microsoft’s grip on enterprise workloads, because organizations finally have somewhere else to run SQL Server using licenses they already own, he noted. Realistically, their dependency shifts to AWS rather than disappearing entirely, as their data ends up living next to AWS’s AI services.

“For a lot of teams, that’s a trade they’re glad to make for the managed infrastructure and the AI tooling they get in return,” Leone said.

Yaz Palanichamy, a senior advisory analyst at Info-Tech Research Group, also pointed to significant improvements stemming from the migration. Notably, it allows organizations to transition away from static, linear, or reactive storage capabilities towards more dynamically intelligent environments.

“The key is to balance the unification of transactional data towards managed AI and machine learning pipelines,” he said.

Enterprises take on new responsibilities

The catch? Leone noted that enterprises now own the task of licensing compliance. Staying current on SA, the Microsoft maintenance contract that makes any of this legal, and the license mobility rules, “become your headache instead of something baked into the bill,” he said.

Furthermore, he added, lift-and-shift has a way of turning into “lift-and-forget” when enterprises move SQL Server as-is and never actually modernize, “so you end up carrying all the old baggage onto a shinier platform.”

Palanichamy also pointed to skyrocketing AWS costs, since operationalizing AI agents requires a considerable amount of data ingestion and querying. This can potentially be cost prohibitive on RDS for SQL Server.

“One aspect to consider would be the relative time to production value, so that enterprises can better handle the management of volatile AI workloads,” he said.

Just one challenge in the AI race

Although AWS frames licensing complexity as a roadblock to agentic AI, analysts say it’s really just one piece of the puzzle.

Organizations are not in an AI-ready state due to a number of factors, Palanichamy noted. This could be total cost of ownership (TCO)-related, a lack of appropriate acceptable use policies (AUPs), or concerns around security and compliance.

If and when enterprises are ready to adopt AI, they must perform thorough needs analysis/process optimization benchmarking exercises to determine what workflows could benefit from the technology, he said, and, conversely, flag workflows where AI could prove “an impediment or potential disservice.”

Leone also pointed out that moving SQL Server onto RDS doesn’t “magically make your data agent-ready,” nor does it make it smarter. The data proximity and managed plumbing are what actually support agents. Sitting data next to Bedrock and the rest of AWS’s AI services means builders “stop wasting time shipping the data around or building one-off integrations every time an agent needs it.”

Ultimately, though, licensing isn’t the real issue when it comes to AI; it’s more of a migration problem. The real roadblocks are messy data, questionable governance, and teams that aren’t ready to run it in production, and, Leone said, “no change to a license bill touches a single one of them.”

I’ve seen a lot of promising AI prototypes fall apart after launch. And it’s rarely because the model was bad. More often, the problem starts much earlier; teams treat the data layer like something they can figure out later.

They’ll spend weeks fine-tuning prompts, testing models and debating evaluation scores, then throw together the retrieval pipeline over a weekend and move on. At first, everything looks great in demos. But a few months later, the system gives outdated answers; the embeddings no longer match the source documents, and nobody fully understands what changed.

What started as an impressive prototype slowly becomes difficult to trust in production. The teams that avoid this tend to realize one thing early: Embedding pipelines are fundamentally a data engineering problem, not an entirely new AI discipline. It’s still ETL (Extract, Load, Transform) at its core, but with embeddings and vector stores as the destination instead of a warehouse.

Once you start looking at it that way, a lot of things become clearer. Problems like versioning, data freshness, lineage and retries stop feeling “AI-specific.” They’re data infrastructure problems we’ve already spent years learning how to solve.

Why do we need embedding pipelines?

Large language models are extraordinary reasoners trapped inside a time capsule. When training ends, the model’s knowledge is sealed. It does not know what your team decided in last quarter’s strategy review. It has never read the support ticket that came in this morning. It cannot find the clause buried on page 47 of your master service agreement. It’s brilliant, but blind to anything specific to your organization.

Layer on top of that a hard context window limit, a ceiling on how much text the model can process in a single interaction, and you have a clear problem: you cannot just hand it everything you own.

The answer the industry converged on is retrieval-augmented generation, or RAG. Instead of stuffing everything into the context window, you build a retrieval layer that fetches only the most relevant pieces of information at the moment a question is asked and passes just those to the model. That retrieval layer is powered by a vector database, and the process that populates it, which is taking raw documents and transforming them into searchable semantic representations, is what I mean when I say embedding pipeline.

Every team building an internal AI assistant, a smarter enterprise search tool, an automated customer support agent or a document Q&A system needs one. The question is not whether to build it. The question is whether you build it like a prototype or like infrastructure.

How an embedding pipeline works

An embedding pipeline has three stages: ingestion, chunking and indexing. Here is what each one means and how I relate them to a typical ETL process.

Ingestion is extraction

Getting your raw content, PDFs, wiki pages, Word documents, database records, transcripts, out of wherever it lives and into the pipeline. This is ETL’s extract stage, almost verbatim.

I see teams cut corners here more than anywhere else, and it’s often where production systems first start to fail. A document gets updated, but the pipeline doesn’t pick it up. A file gets deleted, but its chunks remain in the index, still returning outdated answers months later. And because there’s no obvious error, no one reports it.

The fix is Change Data Capture (CDC). This maintains a manifest of every document you have ingested, a content hash and a timestamp. On each run, we compare sources against that manifest, re-ingest what changed, delete what is gone and treat your document the way you would treat any source table you are syncing incrementally.

Chunking is transformation

Once your documents are in the pipeline, you cannot embed them whole. A 30-page technical report is too long to represent meaningfully as a single vector, and even if it were not, returning the entire report in response to a narrow question would bury the model in irrelevant context.

Chunking is the process of breaking each document into smaller pieces that are focused enough to embed accurately and retrieve precisely. This is ETL’s transform stage, and it deserves the same level of design discipline.

The most common mistake I see is treating chunk size as a default configuration option rather than a product decision. It is not. The right chunk size depends entirely on the nature of your content and the nature of your queries. Dense technical documentation needs finer granularity than a collection of FAQs. A legal contract with clause-level logic needs different treatment than a set of onboarding emails. What works for one document will actively degrade retrieval quality on another.

My strong preference is to treat your chunking configuration as a versioned pipeline parameter, not hardcoded logic. When you change it, and trust me, you will. You need to re-chunk in a controlled, observable way, compare retrieval quality before and after, and roll back if it degrades. That is just good transform-layer hygiene. It is no different from versioning a data cleaning rule or a field mapping.

Indexing is the load

The final stage is where chunked text gets converted into vectors and stored in a vector database where it can be searched by semantic similarity rather than keyword match.

In the conversion step, embedding is handled by a model specifically trained to turn text into dense numerical representations that encode meaning. Two chunks expressing the same idea in different words will produce vectors that cluster close together in that mathematical space. Two chunks discussing entirely different topics will sit far apart. When a user asks a question, the system embeds that question the same way, finds the chunks whose vectors are nearest, and returns them as context for the model to reason over.

That is a genuinely new capability. But the discipline around indexing is not. One data engineering principle I keep coming back to is versioning.

In embedding pipelines, every chunk in your index should be tagged with the embedding model name and version used to generate it, this is non-negotiable. Embedding models evolve, and vectors produced by different versions are not comparable in a reliable way. You cannot safely search across them as if they are interchangeable.

This exact problem shows up when teams upgrade embedding models mid-pipeline without a proper migration plan. You end up mixing vectors from different generations in the same index, and retrieval starts to degrade in ways that are hard to detect. The system just quietly begins returning subtly wrong answers.

I treat an embedding model upgrade the same way I treat a schema migration: Plan it explicitly, execute it in full and validate retrieval quality on a representative query set. The stakes are the same as any breaking change to your data model.

Pipeline observability is not optional

Once an embedding pipeline is running in production, the question shifts from “did it run” to “did it run correctly.” That distinction matters more here than in most pipelines, because failures are rarely loud because the index looks fine, queries return without errors and the system quietly surfaces wrong answers until someone notices the AI has stopped being useful.

The same observability discipline that makes any data pipeline trustworthy applies directly here. Once you treat embedding pipelines as production systems, you stop thinking in isolated steps and start thinking in signals. For example, chunk counts per document become a simple but powerful health check, a sudden drop is usually not a model issue, but a sign of broken ingestion or upstream parsing failures.

You also need a “golden set” of queries with known-good outputs. This runs after every pipeline change, much like data quality checks after a transformation. This is how you catch regressions that don’t show up as explicit failures.

On top of that, you can track lineage: Which embedding model version produced which chunks, and when each document was last ingested. That makes it possible to trace retrieval issues back to specific changes instead of guessing.

And finally, freshness becomes a first-class signal. If documents start going stale beyond an acceptable threshold, that should surface in monitoring long before users experience degraded results.

The metric that ties it all together is retrieval quality over time. Treat it like any other pipeline SLA, measured, tracked and owned.

Summary

Embedding pipelines definitely come with a lot of new language, new tools and a genuinely different capability in the semantic layer. But the funny thing is, the principles that actually make them reliable in production are not new at all.

We have versioning, freshness, quality checks and monitoring. These are problems data engineering has already spent years solving.

The real work is taking that same discipline and applying it to a pipeline that just happens to output vectors instead of rows in a table. Once you start seeing it that way, a lot of the chaos around AI systems becomes much easier to reason about.

That’s the difference between building a cool AI demo and building something people can actually depend on. One is a prototype, whereas the other is infrastructure.

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Artificial intelligence does not advance at the same pace across industries. It presses forward in some directions while lagging behind in others.

Spend time with today’s most advanced AI applications, and this contrast becomes obvious. In software development, AI is quickly becoming ubiquitous. It writes production-ready code, explains obscure libraries, and iterates at a pace human teams have difficulty matching.

But place that same AI model inside a complex customer support workflow or ask it to reason through a nuanced clinical scenario, and the cracks begin to show. Multi-step reasoning falters. Context gets lost. Performance drops in ways that can seem inconsistent with the model’s strengths elsewhere.

These AI models are often similar. They run on similar hardware and are often trained in similar ways. So why the mismatch in performance across tasks? The simplest explanation is also the most overlooked: data.

Software engineering benefits from an immense, structured, and highly visible digital record. Code is written in standardized languages, benefits from robust documentation, is reviewed in public forums, and is discussed at scale. That ecosystem has generated a robust and massively useful pool of training material.

Other fields often do not. For example, healthcare data is scattered across institutions, wrapped in privacy constraints, expressed in multiple modalities, and rarely ready out-of-the-box for AI training. Enterprise workflows are captured in internal systems that were never designed for training AI. Multilingual speech data varies widely in quality and representation.

This imbalance creates what I describe as “the data gap.” This is the distance between what models are capable of in theory and what they can achieve in practice, because the right data does not yet exist in usable form. Closing this data gap may be the most important—and least glamorous—challenge in AI today.

The missing pillar of AI progress

Three forces are driving the recent advances in AI: the models, the chips, and the data.

On the AI models, the field has invested heavily. Major research organizations employ thousands of researchers and scientists who are actively refining architectures, training techniques, and evaluation methods. Breakthroughs are measured in benchmark scores, conference papers, and model performance on human tasks. On the computing chips, the investment has been equally intense. Hardware manufacturers and infrastructure providers are pouring billions of dollars into building and supporting data centers that deliver faster results via large-scale training.

Yet data has not received the same institutional focus for AI development. Conversations with researchers at frontier AI labs share a similar frustration that today’s model capabilities in key use cases, such as healthcare, are limited less by architectural imagination and more by the availability of high-quality, domain-specific data. The bottleneck is not always a lack of ideas, but a lack of reliable inputs.

We are long past scraping the internet for useful data, and this path does not scale. Progress depends on building and curating datasets that reflect the complexity of true lived experience and organizational processes. That work requires both scientific rigor and research specialization in the field of data for AI.

The dataset behind every leap

The history of AI reinforces a consistent lesson: major leaps in model capability follow major leaps in the availability of quality data. From early vision systems that relied on clearly labeled images to today’s language models trained on massive text collections, each major leap has depended on access to more high-quality data.

Architectural innovation alone is rarely enough. The value of these new approaches only emerges when paired with large, structured, and representative datasets that reveal what the models can actually do in practice. Whether in vision or language, progress has depended on the painstaking work of collecting, organizing, and validating the underlying data.

Large language models illustrate this clearly. Their emergence was not just the result of better training techniques, but of access to an unprecedented volume of data. The models did not generate that data. They relied on it. That pattern raises a pressing question for the present: who is building the next generation of foundational datasets?

Across domains ranging from healthcare to audio to agentic task performance, there is no widely accepted blueprint. What constitutes a gold-standard dataset for training an AI agent to handle complex enterprise tasks? What does a clinically meaningful evaluation look like for a model that will assist in medical decision-making? How should multilingual speech data be curated to ensure broad representation and reliable performance?

These are not simple sourcing problems. They are fundamental research challenges that need to be solved.

When data is treated as a commodity

Too often, consequential data decisions are handled like procurement exercises. An organization requests “medical conversations” or “wildlife scenes,” and the request is routed to internal procurement or data sourcing teams, or to external data vendors, who assemble data that appears to match the description. The implicit assumption is that data is interchangeable, that one dataset is as good as another so long as it meets a basic specification.

Actual application suggests otherwise. Seemingly small choices about factors such as inclusion criteria, annotation standards, filtering rules, and validation protocols can dramatically alter downstream performance. Data design shapes model behavior as much as architecture does.

Three structural issues compound the problem:

• Capacity: There are relatively few specialized teams dedicated to building domain-specific datasets at the highest level of rigor. Talent and funding have gravitated toward model development and hardware innovation. Data work often operates in the background, even though it underpins both.
• Design: Constructing a dataset is a distinct discipline from designing a neural network. It requires expertise in experimental design, domain knowledge, and statistical validation. Expecting model researchers to simultaneously shoulder the full burden of data research, while also training and evaluating the models, overlooks the complexity of the upstream task.
• Translation: The researchers who are requesting specific data sources to improve the models are often not the same people responsible for sourcing that data. As a result, nuances and research-backed expertise can often be missing or diluted as requests pass through layers of procurement and vendor relationships. The result can be data that meets the specification sheet, but in fact fails to advance model performance.

The rise of annotation providers and reinforcement learning services has addressed part of the need. Rating model outputs, labeling text, and evaluating structured information are essential for many optimization tasks. But these activities generate data that is carefully constructed for specific, bounded purposes.

The frontier challenges in AI require more. They demand datasets derived from real human activity and organic organizational processes. Such data is complex, multimodal, and sensitive. It is rarely AI-ready by default. And converting it into reliable training and evaluation material is a scientific undertaking.

The need for scientific rigor for the AI data layer

If high-quality data is a central bottleneck, then scientific rigor is part of the solution. Just as leading model-builders have dedicated research labs and hardware has dedicated development ecosystems, the data layer for AI requires focused, scientifically-grounded institutions.

This means engaging directly with core questions like dataset design, evaluation methodology, and quality control. The conversation cannot end at volume; it must address data structure, representativeness, and expert validation.

Dataset construction must be approached as experimental design. Protocols must be documented and validated. Evaluation frameworks must test whether the dataset truly reflects the intended applications.

The field also requires standards and benchmarks that reflect real-world complexity, not simplified proxies. In healthcare, for instance, evaluating a system intended for clinical assistance with generic question-and-answer tests is insufficient. Real-world clinical environments involve multimodal inputs and contextual judgment. Benchmarks must reflect that reality if they are to function as meaningful gates before deployment.

Quality measurement is another crucial frontier. Finance relies on standardized metrics such as credit scores to assess risk. AI lacks an equivalent for datasets and benchmarks. Developing clear methodology to quantify dataset quality and evaluation reliability brings clarity to model assessment.

The criteria for evaluating a multilingual audio library will differ from those of a multimodal oncology dataset. Yet the underlying principle remains constant. Better models require better-defined, better-measured data.

The risks of getting it wrong

As AI systems move closer to high-stakes deployment, weak data practices carry tangible risks.

Benchmarks cannot be created with the same data that is used for training—that’s giving the test answers to the model ahead of time. Scaling data volume without prioritizing data quality and selection diminishes model performance gains, and can even bias against or omit underrepresented populations. These are methodological challenges, and ones that must be solved.

The rigor required at the data layer may not attract headlines. It does not typically lend itself to dramatic product launches. Yet the data layer for AI is foundational to trust, safety, and sustained progress for all AI progress.

An ecosystem for the data era

No single organization can resolve the data gap alone. What is needed is an ecosystem of AI data labs and research groups, each focused on different domains and challenges but united by a commitment to scientific discipline. These institutions would collaborate with model researchers and domain experts who would tackle challenges such as dataset contamination, factuality, groundedness, de-identification, international representation, and bias. They would design benchmarks that mirror real-world complexity rather than simplified abstractions.

AI’s trajectory will not be determined solely by larger models or faster chips. It will be shaped by the datasets we construct, the standards we adopt, and the rigor we apply at the foundation. The uneven frontier we see today reflects an uneven data landscape. Bridging the gap requires deliberate, research-driven dataset design.

If we want AI systems capable of operating reliably in clinical contexts, navigating enterprise workflows, and functioning responsibly across languages and cultures, we must treat data for AI as a first-class scientific endeavor.

AI models have their research labs. AI chip-builders have their fabrication plants. AI data needs institutions of equal seriousness and ambition.

—

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.

As enterprises move from AI experimentation to production deployments, one challenge is becoming increasingly apparent: AI systems are only as reliable as the business context they operate in.

Snowflake is attempting to address that problem with Horizon Context, a new set of semantic and metadata-management capabilities, currently in preview, that it unveiled Tuesday at its annual Snowflake Summit conference.

Artin Avanes, head of core data platform at Snowflake, said that the offering, launched as part of Horizon Catalog, the company’s existing data discovery, management and governance suite, collects metadata from across an enterprise’s data estate, enriches it with business definitions, relationships, lineage, and governance information, and makes that context available across AI and analytics systems.

These capabilities, according to Avanes, build on Snowflake’s acquisition last year of Select Star, a metadata management startup known for its integrations with database systems such as PostgreSQL and MySQL, business intelligence tools like Tableau and Power BI, and data pipeline/orchestration tools such as dbt and Airflow.

Reducing operational complexity of agentic workflows

For CIOs, Horizon Context should reduce operational complexity because it will provide a governed map of their organization’s data estate, said Stephanie Walter, practice lead of the AI Stack at HyperFRAME Research.

“The value is not simply cataloging where data lives; it is giving AI systems the metadata, lineage, permissions, and business context needed to retrieve the right data safely,” Walter said.

In fact, according to Robert Kramer, managing partner at KramerERP, the challenge of missing context for analytics and reporting is not new for enterprises.

“Most enterprises were stitching together catalogs, BI semantic layers, governance tools, lineage, access controls, dbt models, security policies, and custom integrations. The issue was not that these pieces did not exist. The issue was that they were fragmented,” he said.

That duct-taping together of different pieces, said Mike Leone, principal analyst at Moor Insights & Strategy, is the actual reason why most teams in an enterprise would end up with slightly different versions of a business metric, such as monthly active users, resulting in AI agents also being inconsistent downstream.

But, said Walter, the stitching together of different catalogs and semantic layers only works for semi-autonomous workflows. “What changed with AI agents is that those systems increasingly need access to context at runtime rather than through documentation and human interpretation,” she said. “Snowflake is trying to pull those pieces closer to the data platform, so context, semantics, access control, and execution are part of the same operating environment”

Automatically maintaining business context for AI agents

To supplement Horizon Context, Snowflake is also adding Semantic Studio, currently in private preview, to help enterprises reduce the effort required to build and maintain business context for agents and agentic workflows.

“Semantic Studio is a core part of the Enrich layer of Horizon Context, providing the AI-assisted workspace where teams define, test, and publish that business logic,” Avanes said.

The Enrich layer itself, which consists of companion capabilities like the Semantic View Autopilot, automatically layers intelligence on data assets, providing insights such as which assets are most trusted, how they connect, what they mean, and how to correctly calculate metrics, Avanes added.

According to Leone, Semantic Studio solves a critical challenge: “It will reduce the burden of SQL-savvy data engineers and let business owners author the shared definitions. That’s where most semantic-layer projects die today.”

For Walter though, there is still a caveat to these offerings.

“While these offerings reduce complexity, they do not remove the work. Someone still has to decide which metrics are authoritative, which data products are trusted, and who owns the business definitions,” she said.

Addressing security and governance concerns

Alongside these updates, Snowflake is also trying to address security concerns around agentic workflows with new capabilities inside Horizon Catalog’s Trust Center.

These capabilities, which are focused on AI Security Posture Management (SPM), include a new agent identity capability that enables enterprises to distinguish between human activity within a session and actions taken by an AI agent operating on a user’s behalf, Avanes said.

“This should provide greater transparency and auditability for agents, while also enabling enterprises to apply existing data access controls, such as dynamic masking and row access policies, on an agent-by-agent basis,” he said.

Another new capability, data exfiltration policies, currently in private preview, will help enterprises prevent unauthorized movement of sensitive data. These policies are part of Horizon Catalog’s broader governance framework, and will allow enterprises to define controls around how data can be accessed, shared, and moved across systems, Avanes said.

For enterprises, Walter noted, these capabilities could help address one of the biggest barriers to deploying AI agents in production: governance.

“Data exfiltration controls, AI Security Posture Management, and other centralized AI controls help CIOs move from experimentation to production by giving security teams a way to see and govern how AI workloads interact with enterprise data,” she said.

Echoing Walter, Leone pointed out that the “security story” is the gating issue for almost every agent deployment that Moor Insights & Strategy is currently tracking.

“Features such as data exfiltration policies and AI SPM are what convert a CISO from a ‘no’ to a ‘yes’ on moving an agent deployment into production,” he added.

This article originally appeared on CIO.com.