AI agent orchestration platforms coordinate role-based and task-based AI agents, along with the tools, data, and people they depend on, into multistep workflows. These platforms are highly important for organizations scaling from handfuls to thousands of AI agents running in production.  

Two open standards do the connective work: MCP (Model Context Protocol) gives agents governed access to tools and data, while A2A (Agent2Agent) lets agents discover and delegate to one another, including agents built on other platforms. The orchestration layer sits on top, adding the routing, shared state, guardrails, governance, security, and observability needed to run workflows that range from fully autonomous to human-in-the-loop.

AI orchestration platforms may be the hottest AI technology of the year. In researching this article, I identified more than 60 commercial and open source platforms that businesses can use as a control plane to manage work between AI agents, people, and automations.

Like data fabrics and automation platforms, I suspect enterprises will utilize more than one AI agent orchestration platform. Platforms are being released by hyperscalers and solution providers in enterprise SaaS, process automation, customer experience, data management, AIops, and IT infrastructure. Development-centric platforms include open source, commercial, and no-code integration solution providers.

Here are five considerations when reviewing AI agent orchestration platforms.

1. Observable control, oversight, and trust

AI agent orchestration platforms are non-deterministic and leverage AI capabilities to coordinate responses and actions across AI agents. One area to evaluate is how administrators implement controls and guardrails over which AI agents can coordinate with others and under what circumstances. Additionally, platforms should also have controls on when and where people should be involved before taking action.

“CIOs should focus on how the AI orchestration platform clearly applies controls over autonomous decision-making,” says Heather Richards, global vice president of go-to-market strategy at Verint. “Ideally, the platform makes it easy to define who or what can take actions, how decisions are approved, and where accountability sits when something goes wrong. If orchestration doesn’t have built-in governance, visibility, and human override, it will scale risk faster than it scales value.”

Observable AI agents are primary capabilities for tracing how they interact and where decisions are made. But even more important is to review how platforms govern access to the context layer, which can include retrieval-augmented generation (RAG) for language models, knowledge graphs, and semantic layers.

“When evaluating an AI orchestration platform, organizations should consider whether governance and observability were built into the architecture from day one,” says Caitlin Schuman, director of AI strategy and customer innovation at Presidio. “A strong platform should make it clear what context is being used and should have a control layer that routes work across systems, agents, and humans.”

Deploying trustworthy AI agents is important for gaining employee adoption. Charles Crouchman, chief product officer at Redwood Software, suggests evaluating how an AI agent orchestration platform establishes trustworthy operations with enterprise resources. He recommends asking these five questions:

• Can it connect to the systems actually running your business?
• Can it be trusted to execute mission-critical logic across your ERP, supply chain, and finance platforms?
• Does it provide deterministic guardrails for non-deterministic AI, so agents can’t go rogue in production?
• Is it model-agnostic, so you’re not locked into a single LLM or agent framework as the landscape shifts?
• Can you govern at scale with full audit trails, observability, and accountability?

“Validating these answers moves you from disconnected AI  reasoning to real execution, empowering you to take the next step towards an autonomous enterprise,” says Crouchman.

2. Secure and resilient operations

AI agent orchestration platforms centralize a growing number of operational workflows, so it’s important to evaluate whether their security, performance, reliability, and resiliency meet compliance and non-functional requirements.  

“Deploying agents is the easy part; the hard part is ensuring they operate safely, consistently, and in coordination with the people and systems around them,” says Daniel Meyer, CTO at Camunda. “Orchestration platforms should enforce controls between an agent’s decision and its action, handle long-running processes without losing state, and maintain a full audit trail natively.”

Organizations should also consider how platforms support agentic ops practices for identity management, monitoring, AI agent accuracy, and incident management.

“Don’t just seek solutions that coordinate workflow or handle the life cycle of an agent; also seek solutions that get the answers agents need faster, with more accuracy, all while meeting essential security and compliance requirements,” says James Urquhart, field CTO and technology evangelist at Kamiwaza. “A platform that securely coordinates context gathering and result formulation across widely disparate infrastructures and data sources is essential, not only to the performance of AI in the enterprise, but also to its agility.”

3. Integrated testing and feedback

Testing AI agents requires validating changes before deployment, just as with continuous testing for applications and APIs. But it also requires evaluating prompts, responses, and actions in production and ensuring that agents aren’t drifting from expected parameters or going rogue. One area in which AI agent orchestration platforms differ is how they support testing AI agents, monitoring them in production, and providing a centralized source of feedback to support accuracy improvements.

“When selecting an AI orchestration platform, don’t overlook where the software it produces actually gets tested and validated,” says Jean-Philippe LeBlanc, senior vice president of engineering at CircleCI. “AI can accelerate every stage of development, but without rigorous, automated validation integrated into the delivery pipeline, you’re compounding risk at the same rate you’re compounding velocity.”

Armando Franco, senior director of cloud and platform modernization at TEKsystems Global Services, says, “The criterion that actually matters is whether continuous outcome evaluation is a first-class capability of the platform itself, because without it, iteration speed collapses and the program stalls.”

4. Interoperability and open standards

MCP and A2A are two ways AI agent orchestration platforms support open standards and enable connecting to an ecosystem of agents. Many platforms also allow developers to select and replace the underlying AI models and to choose from a range of AI code-generation tools. These flexibilities ensure teams can optimize around performance, accuracy, compliance, costs, and other future considerations.

“When evaluating an AI orchestration platform, we look first at composability and interoperability,” says Rajesh Arora, chief data and analytics officer at Principal. “The real test is not how many features it offers today, but whether it can connect models, data sources, agentic solutions, and workflows in a way that adapts to our AI strategy, tech stack, and changing business needs.”

Other interoperability criteria to review include the platform’s AI agent cataloging capabilities, how permissions are configured dynamically, and whether prebuilt connectors are available for the required integrations.

5. Vendor viability and road map

Leaders recognize that AI is currently reshaping business more than driving transformation. To be successful, organizations require AI governance that keeps up with strategy and doesn’t lag too far behind. The same is true for AI agent orchestration platforms, so it’s important to review their release notes and road maps to see whether providers strike a reasonable balance between innovation and governance.

“The right orchestration platform provides a unified policy layer that follows work across agents, workflows, and AI tools, enabling your teams to build freely while IT and security maintain full visibility at the action and output levels,” says Brandon Sammut, chief people and AI transformation officer at Zapier. “If your governance can’t keep pace with how fast your people are building, you’ll either slow them down or lose sight of what they’re building.”

Since AI agent orchestration platforms are a new category, technology leaders should partner with their financial, legal, and compliance colleagues to assess vendor viability risks. In addition, reviewing customer adoption and support capabilities is important as top solution providers will continue to evolve their platforms.   

“A mature provider offers both a stable platform and the customer support you’ll need, and with a large customer base, they’ve already hit countless edge cases that can smooth your own implementation,” says Hannes Hapke, director of the 575 Lab at Dataiku. “Assess maturity by looking at funding and financial backing, the clarity and consistency of their public road map, and the size and activity of their community. An engaged user base, active forums, and a healthy ecosystem of integrations all signal a provider that will still be standing when you scale.”

Many organizations are still early in adopting AI agents and transitioning proofs of concept into production. But for those deploying a growing number of AI agents across many platforms, selecting an AI agent orchestration platform enables scaling workflows, operations, and governance. 

For years, the public cloud was the default for new workloads because its convenience, elasticity, and breadth of services made sense. However, as AI’s strategic importance grows, its economics and infrastructure are changing. Tesla is one of the clearest examples of a company deciding that AI is too important, too expensive, and too central to its business to leave largely in the hands of a third-party cloud provider.

 At the center of Tesla’s strategy is a simple idea. If AI is key to how you build your products, run your business, and define your future, the infrastructure that powers AI becomes a strategic asset. It is no longer just plumbing but part of the product itself. Tesla’s models, databases, applications, and workflows increasingly rely on infrastructure that is built, hosted, and managed by Tesla. That means the company has direct control over the hardware, the software stack, data movement, performance tuning, and security posture. For a company that depends on AI to support autonomy, robotics, manufacturing intelligence, and future product direction, that control matters.

This is the real heart of the matter. Tesla is not treating AI as a side project or a feature layer added on top of an existing business. AI is central to Tesla now and into the future. It is a force multiplier, but more than that, it is an essential aspect of product development, operational efficiency, automation, and competitive differentiation. Once a company reaches that level of dependence on AI, the conversation around infrastructure changes very quickly.

Public cloud is attractive because it provides a complete AI ecosystem on demand. You can provision compute, storage, training environments, managed services, orchestration tools, and deployment pipelines without building much yourself. That is why I often call public cloud “the easy button” for AI. It is fast, convenient, and feature-rich. But convenience comes with a premium, and for many companies moving deeply into AI, that premium is becoming very hard to justify.

Cost is driving AI out of the cloud

One of the biggest forces driving this shift is price. Many enterprises moving into AI are shocked by what public cloud providers charge for AI infrastructure. Training clusters, inference engines, storage, networking, observability, and support services all add up quickly. What begins as a convenient path to experimentation can become an extremely expensive operating model when AI moves into production at scale.

In my experience during the past 15 years, public cloud is often at least twice as expensive as comparable private infrastructure for sustained workloads. That is not true in every case, and it depends heavily on utilization patterns, architecture, and operational maturity. However, for large, predictable, always-on AI workloads, public cloud economics often become difficult to defend. The markup associated with convenience, elasticity, and managed ecosystems is substantial.

In response, companies are increasingly exploring alternatives. Some are moving toward neoclouds that specialize in AI infrastructure. Others are evaluating sovereign cloud options for control, locality, or compliance reasons. Many are revisiting private cloud infrastructure for the most strategic and cost-intensive workloads. Tesla is becoming the poster child for how successful that approach can be when a company has the scale, the sophistication, and the long-term commitment to execute it well.

Control, governance, and security

Cost is only one part of the equation. Control is the other major driver. When Tesla runs its AI infrastructure on equipment it owns and operates, it gains much tighter control over performance, data handling, workload placement, governance models, and operational priorities. That matters a great deal when the workloads involved are mission-critical and directly connected to the future of the company.

A privately controlled AI environment can provide better security because the organization has direct oversight of the infrastructure stack. It can provide better governance because data, models, and workflows remain inside systems the enterprise fully controls. It can also improve reliability and performance tuning because engineering teams can optimize specifically for their own AI pipelines rather than adapting to the generalized patterns of a shared cloud environment.

Of course, many people are quick to point out that running your own private infrastructure comes with significant labor and cost. They are not wrong. Building and operating private AI infrastructure requires capital, engineering skill, facilities, procurement discipline, operational excellence, and long-term commitment. This is not a shortcut, nor is it easier than public cloud. In many ways, it is harder.

However, the point is that for sophisticated companies with large-scale, steady-state AI needs, it can be worth it. Better control, better governance, better security, and ultimately lower cost can justify the additional operational burden.

The future of AI infrastructure

What makes Tesla so important in this discussion is that the company chose this route because AI is inseparable from its business strategy. Many other enterprises are heading in this same direction. As AI becomes less experimental and more operational, the infrastructure conversation shifts from convenience to economics, control, and differentiation.

Not every company should follow Tesla’s path. Many enterprises are not ready to build, host, and manage their own AI environments. Many lack the scale to justify it. Many still benefit tremendously from the agility of public cloud. But for organizations where AI is becoming central to products, services, and competitive advantage, Tesla’s strategy is increasingly relevant.

There are three things every enterprise should think about when considering ownership of its own AI infrastructure:

• First, understand the operational burden in full. Private AI infrastructure requires teams, processes, facilities, and discipline that many organizations underestimate.
• Second, know the economics of your workload patterns. If AI demand is large, steady, and strategic, the cost advantages of ownership may be compelling.
• Third, think beyond cost alone and focus on control. If AI is core to your future, owning the infrastructure may offer strategic benefits in governance, security, optimization, and long-term independence that public cloud cannot easily match.

Tesla’s strategy is not for everyone, but it is a persuasive example of what happens when a company decides that AI is too important to rent forever. Public cloud remains the easy button, and for many organizations, that will be enough. But for companies that see AI as fundamental to how they will compete, private infrastructure may turn out to be the smarter choice.

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.

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.

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.