A growing divide among leading AI companies over how to secure increasingly powerful models is beginning to translate into challenges for enterprise IT, with implications for how organizations access, deploy, and govern AI systems.

The latest flashpoint came after Meta CEO Mark Zuckerberg called for neutral evaluators to independently test AI models, pushing back on calls from rivals to slow development or tighten coordination.

“trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn’t focus on alignment will fall behind,” Zuckerberg wrote in a post on X.

“Engaging independent evaluators and advisors is industry best practice,” he added, noting that Meta already does this in several areas.

His comments follow a series of public proposals from AI industry leaders including Dario Amodei, who argued for a more cautious pace of development, and Sam Altman, who called for collaboration on safety standards.

The debate has intensified amid disclosures from AI labs and policymakers on potential misuse of advanced systems. Anthropic has said it restricted attempts to use its Claude models in sensitive domains, while OpenAI has engaged with policymakers on AI-related risks, according to company statements and reports.

Enterprise concerns

While the debate is often framed as a choice between slowing innovation and strengthening oversight, analysts said enterprises should focus less on which approach prevails and more on the operational consequences already taking shape.

“Divergent safety approaches will make access to advanced AI models less predictable, rather than producing an industrywide slowdown,” said Sushovan Mukhopadhyay, director analyst at Gartner. Vendors are likely to apply different release schedules, regional availability, access tiers, and usage restrictions, he said, meaning enterprises could encounter similar capabilities “at different times and under materially different conditions.”

Mukhopadhyay said enterprises should plan for variability in access rather than assuming consistent availability across providers or geographies.

“I read this week as the point where frontier AI became a managed supply,” said Bhupendra Chopra, chief revenue officer at Kanerika. “For three years CIOs could assume the next model would simply show up. A frontier model now behaves more like a critical component from a supplier whose delivery dates depend partly on outside reviewers and export rules.”

Chopra added that “any AI roadmap built on a specific model arriving on a specific date is carrying supply risk it hasn’t priced.”

Security pressure builds regardless of slowdown

Analysts said slowing development alone is unlikely to materially change enterprise risk, particularly as open-source models proliferate.

“The biggest point isn’t the pause itself. It’s that the leaders of AI companies are agreeing on something,” said Nikhil Gupta, founder and CEO of ArmorCode.

Gupta said the threat landscape has already shifted. “Even if companies hit pause, open-source AI models are already out there,” he said. “I’m not convinced slowing down some companies meaningfully changes what adversaries can do.”

“Even if AI development slows down tomorrow, security must accelerate,” Gupta added. “The job of securing these systems has effectively gotten ten times harder.”

A new ‘AI assurance’ layer emerges

The focus on evaluation is driving what analysts described as an emerging “AI assurance” layer, where third parties assess models for safety and compliance.

“A distinct AI assurance layer is likely to emerge, but enterprises should not expect a single certification to establish that an AI system is safe,” Mukhopadhyay said. “Enterprise risk also depends on data, system instructions, tools, agents and deployment controls.”

Chopra said enterprises risk misinterpreting such evaluations. “Procurement teams may see a third-party evaluation and treat the model as vetted,” he said. “Within a year it becomes a checkbox.”

Instead, he said, enterprises will need to run their own validation. “CIOs who get ahead will test each model against their own data before it touches production.”

Fragmentation complicates multi-model strategies

For CIOs pursuing multi-vendor strategies, differing approaches across providers could introduce additional complexity.

“Fragmentation was already the default. Safety divergence deepens it,” Chopra said.

He said risk is most acute during transitions. “For an enterprise running several models, the exposure sits in the handoff,” he said. “When a model is delayed or replaced, the system can behave differently.”

“I’d rank untested model substitution above vendor lock-in,” Chopra said.

Gupta said open architectures will be important. “The framework needs to be open, not locked to any single vendor,” he said.

Mukhopadhyay added that enterprises should prepare for models becoming unavailable or restricted.

CIOs urged to build resilience

Analysts said enterprises will need to design AI strategies that can adapt to changes in availability, pricing, and governance.

“For critical applications, CIOs should separate application controls and business logic from the underlying model,” Mukhopadhyay said.

Chopra emphasized flexibility. “A routing layer between applications and model providers turns switching into configuration work,” he said, adding that contracts should cover deprecation timelines.

He also pointed to pricing implications. “Scarce access to the frontier starts to carry a premium,” Chopra said.

One of the first questions I ask devops organizations is to walk me through their development and operations standards. What are the non-negotiable devops practices? What are the data governance first principles? What observability standards are in place? How is security embedded in devops? This is the starting point. From there, we might review standards on how functional requirements are written. For businesses developing AI agents, I’ll ask about their non-functional requirements (NFRs) and how testing is performed.

Now that many devops organizations are using AI code generators, vibe coding, and applying spec-driven development practices, the questions increase. How does the AI know your organization’s standards? What processes monitor and enforce these standards? What are the developer responsibilities for owning the outcomes?

“Generative AI will happily hand you the movie set of an app with looks of a house from the street. The doors and windows open, and the demo runs clean,” says Mike Toole, director of security and IT at Blumira. “Behind the facade, there’s no plumbing and no wiring: no input validation, no auth boundaries, nothing actually holding it up. If your only gate is ‘does it run and look right,’ you’re shipping a set, not a building.”

One report shows that 92% of developers use AI coding tools daily in 2026 and that 41% of all global code is now AI-generated. Will all that code deliver value, operational issues, or mounting AI debt? I spoke to several experts about how to keep AI-generated code aligned with organizational standards and how to define an AI code generator’s context beyond just functional requirements.

Document like a construction specification

A general contractor in the construction industry will review building information models that provide a view of what they are developing. But many of the details are in the construction specifications, including component stipulations and performance requirements. Devops teams without equivalent documentation for their organization, architectures, and applications may have gotten away with this omission in the pre-AI era. But institutional knowledge and compliance requirements are the context AI code generators need before you share the first functional prompts with them. 

“AI-generated code stays aligned with engineering standards only when those standards are explicit, available, and difficult to bypass,” says Eddie Zhou, founding engineer at Glean. “Put architecture, approved libraries, data-handling rules, security controls, naming conventions, observability, test expectations, and non-functional requirements into specs and automated checks. Then, ensure you give the model that context but treat its output as untrusted until it passes the same reviews, tests, scans, and production monitoring as human-written code.”

Overcoming tribal knowledge was always challenging as devops teams had too much pressure to push features and little incentive to maintain documentation. Two of my top AI leadership skills are change management and agile collaborative leadership, because devops teams need practice alignment while introducing AI capabilities.  

Zhou adds, “AI amplifies ambiguity and weak governance as efficiently as good engineering. If standards live only in tribal knowledge, AI will expose that problem, not solve it.”

Encode non-functional requirements as automated tests

The problem with requirement documents and reference architectures is they aren’t directly enforceable with human or AI developers. Once a devops team agrees on standards and documents them, the next step is to develop approaches to encode them as automated acceptance criteria in CI/CD pipelines.

“AI code generators and human developers optimize for functional correctness—making the feature work—and quietly skip the non-functional requirements where most of your real standards live, including performance, security posture, error handling, observability, and auditability,” says Michael Bevilacqua, vice president of AI product management at Adeptia. “Encode your NFRs as executable acceptance criteria that generated code must pass before it merges, so ‘aligned with our standards’ becomes a gate the pipeline enforces on every change.”

An example: Performance requirements stating that all web pages should have a total page weight under 2MB can be validated during the pull request, and requiring the time to first byte (TTFB) to be under 800ms can be automated in CI/CD in a staging environment.

Requirements for stakeholders and AI

Architecture and automated non-functional requirements covering performance, security, and other organization-level compliance requirements should become the base-level context standards shared with AI-code generators. The next set of considerations for setting accurate contexts starts with application- and agent-specific requirements, their intended use cases, and specific compliance guardrails.

“CIOs are mandating AI to build apps faster, but many are realizing speed isn’t the bottleneck because more code doesn’t automatically create business value,” says Matt Healy, senior director of product strategy and marketing at Pegasystems. “Before developers write any code, organizations must understand customer needs, assess legacy systems, stay current with regulations and best practices, map enterprise architectures and integrations, and align business and IT.”

Consider three stages of understanding requirements. Once stakeholders set business value and intent, developers must first align on an implementation strategy and discuss performance, cost, and other trade-offs. Then, developers should reconvene with stakeholders and review implementation trade-offs. Healy adds, “On large projects, defining the solution and success metrics can take as much time as building it. Leaders aren’t just using AI to accelerate development, but to help executives, business analysts, product owners, and IT teams collaborate more effectively.”

The third stage is gathering the goals, user requirements, and implementation approach into a development-specific context. But we’re not done yet, because data requirements and operations considerations must also be included in the context fed to AI code generators. 

Build governance into the specs

How the application or AI agent being developed can use the data it has access to requires clear specifications. Data governance programs should have standards based on data sets, defined roles, entitlements, and usage specifications.

Kash Mehdi, vice president and field CTO at Reltio, says the teams that are pulling ahead treat data governance as an input to AI, not an audit after it. “They ground their tools in trusted, semantically consistent data models, approved specs, and security policies, so the AI inherits the organization’s standards instead of inventing its own. Without that governed context layer, you don’t scale from a handful of AI-assisted workflows to thousands; you just accumulate inconsistent code faster,” Mehdi says.

Here are two options to consider in creating standards: First, establish repurposable data sets as data products with defined integrations and usage rules. Organizations with many data sets and integration points should consider data fabrics. The second (and key requirement) is to standardize the format of data contracts, using human- and machine-readable formats so that data owners, users, and AI code generators operate from a consistent set of principles.  

“AI writes code that compiles and passes tests but quietly ignores your data contracts, so the failures show up in production as corrupted state, not build errors,” says Ed Huang, cofounder and CEO of TiDB. “The fix is to make governance machine-checkable, including schema constraints, access policies, and data-quality rules enforced at the database layer where the AI can’t route around them, not documented in a wiki the model never reads. Treat the database as the enforcement point for your standards, and it stops mattering whether a human or an agent wrote the query.”

Observability and testing first

When AI generates the code, a best practice is to use static application security testing (SAST) and other code analysis tools to perform automated code reviews. Another best practice is to put applications through penetration testing and other application security validations. But many of these tools are outside in view of what the AI developed. To get an inside view, observability and testing requirements must be included as a standard, with specific requirements around the key transactions you’re asking the AI to develop.

Sanjay Gidwani, CEO and founder at Kosmos, says, “More code volume just means more time spent investigating, and the fix isn’t reading the code more carefully before it ships. It’s connecting the observability signal, the thing that broke, back to the specific change that caused it. Without that link, you’re doing the same manual investigation as before, just with a bigger haystack.”

Testing requirements must go beyond generating automated tests, using synthetic data to expand the test patterns, and implementing continuous testing. “Instead of testing one input against one output, test whether an invariant holds across a wide range of generated inputs, which is a good fit for AI’s unbounded input space and non-deterministic outputs,” says Lior Gavish, cofounder and CTO at Monte Carlo. “I treat it as one layer in a stack with evals mapping the input space, property-based testing stress-testing it for edge cases and brittleness, and observability on live traffic checking whether production is drifting from the properties you defined as non-negotiable.”

Package and evolve the standards

Ed Frederici, CTO at Appfire, recommends codifying standards as machine-readable rules, including linters, formatters, and type checks. Merge the organizational and application standards into one context shared with the AI code-generating tool. His other recommendations:

• Enforce standards in continuous integration so violations block merges.
• Give the AI persistent context, such as CLAUDE.md, style guides, and architecture docs.
• Keep changes small and reviewable.
• Require human review of every pull request because AI-generated code must get the same scrutiny as any contributor’s. No exceptions.
• Back this approach with solid test coverage to catch regressions.
• Periodically audit for drift as your standards evolve.

It’s also important to remember that as code must be extended and maintained, so must the underlying development standards. “AI-generated code only stays aligned with your standards when it’s powered by a context engine that keeps learning, not a static snapshot of your rules,” says Itamar Friedman, CEO and cofounder at Qodo. “That means an engine that continuously adapts to your codebase’s evolving standards, your team’s PR history, and the prior decisions your engineers have already made, so the AI isn’t just following instructions but actually understands how your team works.”

As more devops organizations rely on AI code-generating tools, developers have an important opportunity to develop the standards and automated validations required to ensure what is built meets engineering and compliance requirements.

The newest member of the corporate workforce never sleeps, never asks for a raise, and can be cloned before lunch. It can also confidently make the wrong decision thousands of times before anyone notices.

That helps explain why some technology leaders have started talking about AI agents as employees, co-workers, or interns rather than software. Once an agent can update records, issue refunds, send communications, or act across multiple enterprise systems, deploying it no longer looks much like installing another application.

Recent Harvard Business Review articles have pushed that analogy further. One argues that agents should be treated like a new workforce, with defined roles, authority, sources of truth, supervision, and audit trails. Another recommends giving agents names and job descriptions, onboarding them, evaluating their performance, and increasing their responsibilities after they prove themselves.

But there is a catch: People may start believing the metaphor.

A separate HBR study of 1,261 managers found that when AI was framed as an employee rather than a tool, managers with experience working around AI agents took less personal responsibility for results and assigned more responsibility to the AI. They also escalated more problems and caught fewer errors. In the study, 31% of respondents said their companies already framed AI as a teammate or employee, while 23% said agents appeared on organizational or work charts.

For IT leaders, the answer may therefore be to borrow freely from the employee-management playbook — without extending the metaphor too far.

Don’t put software on the org chart

Amy Loomis, group vice president at IDC, draws a hard line around one word: responsibility.

Humans, she says, have obligations to their companies and to one another, and they bring an understanding of corporate culture and values that an agent can mimic linguistically without possessing.

That makes calling an agent a co-worker potentially more than harmless anthropomorphism. The label suggests a peer relationship and can encourage people to cede judgment to a system that’s very good at giving what was requested, Loomis says, but not necessarily at recognizing what was actually needed.

“Accountability is something that has qualities associated with it that are uniquely human,” she says. “Agents may be given permission to access data, but the accountability for what they do with that access lies with the person who gave the agent access, not the agent itself.”

That distinction still leaves plenty to borrow from employee management. It makes sense to give an agent traceable access and tightly scoped permissions — and to institute a kill switch to prevent the agent from having continued access. But Loomis finds it more effective to think in terms of giving an agent an operations charter rather than a human identity: define what the system does, what it may access, and what humans remain responsible for.

In other words, give the agent a security badge. Just don’t put its picture on it.

The employee metaphor has a job to do

Raja Iqbal, founder of agentic AI governance company Ejento AI and co-author of the HBR article “To Scale AI Agents Successfully, Think of Them Like Team Members,” is closer to Loomis than the headline suggests.

Iqbal says the metaphor is intended to put CIOs, CISOs, and other technology leaders into the right “cognitive posture.” Thinking about an agent as a team member, he argues, can remind an enterprise to impose familiar management disciplines: a unique identity, clear human oversight, defined authority, spending limits, approved information sources, and an audit trail.

If another term produces the same discipline, he’s fine with that too.

In fact, Iqbal explicitly rejects synthetic employees with human personas and seats on the org chart. That creates exactly the accountability problem critics worry about: “Bob did it” becomes an excuse even though Bob is software.

“The metaphor is dangerous when it’s decorative,” Iqbal says. By that, he means companies get into trouble when they adopt the human trappings — names, personas, or the language of colleagues — without also imposing the supervision, limits, and accountability that make the analogy useful.

His preferred distinction is simpler: the agent needs a technical identity so the enterprise can reconstruct what it did, while a human must remain responsible for those actions. “The identity is the audit primitive, and the human owner is the accountability primitive,” he says. Put more plainly, the agent identity answers what acted; the human owner answers who is accountable.

The employee analogy becomes more useful again when deciding how much freedom an agent should receive. Iqbal recommends graduated autonomy. An agent might begin with a human approving every action, progress to performing low-risk actions independently, and eventually receive bounded autonomy if its observed performance justifies it. A serious error can send it back down the ladder — or out the door entirely.

That sounds a lot like a probation period for a new employee. It’s also simply good software governance for a probabilistic system.

At DXC, professional agents are software

Russell Jukes has had plenty of opportunity to test that distinction. As chief digital and information officer at DXC Technology, he oversees AI in an IT services organization with roughly 115,000 employees.

“We think about [an AI agent] as a superpower, not as a person,” he says.

DXC separates agents into two categories. Personal agents work for individual employees and generally operate using the employee’s identity and access. Professional agents are enterprise systems built for broader workflows. On one of DXC’s major AI platforms, employees have so far created roughly 8,000 personal agents, compared with about 100 professional agents.

The professional variety receives much stricter oversight. It can have its own nonhuman identity, credentials, permissions, and authority boundaries. Activity passes through control planes that allow DXC to see what agents are doing and, when necessary, shut them down.

Jukes thinks many of these agents will eventually disappear from users’ view altogether. An agent might scan internal resource requirements overnight and recommend employees for projects. Nobody needs to greet it in the morning or consider it a colleague; they simply receive the result.

That’s where he parts company with the worker metaphor.

Jukes says he learned early on that AI cannot be deployed like conventional SaaS. His first attempt followed the familiar software model: make the application available, let employees use it within the prescribed workflow, and expect adoption to follow. “I did that, and nobody used it,” he says. The experience convinced him that AI requires a different approach, and he vowed to “never deploy AI like it’s SaaS again.”

Conventional SaaS is largely deterministic: the software constrains users to a defined workflow and produces predictable outputs. Agentic AI is different. It can interpret a goal, choose among tools and data sources, and decide how to get to an outcome. For Jukes, that means CIOs have to design the policies, permissions, data access, guardrails, and authority around the agent rather than simply deploy another application.

That also creates a clearer chain of responsibility. If a person approves the agent’s action, Jukes says that person owns the decision. If an autonomous agent behaves according to a business policy, accountability belongs with the people responsible for that policy. If the technology fails to operate as designed, responsibility moves to IT.

The agent itself never gets handed the blame.

Maybe treat it like an intern after all

Nina Tatsiy, global CIO of Quadient, an automation technology company, takes a position somewhere between Iqbal and Jukes. She calls agents interns, but deliberately uses the term as an analogy, not an organizational designation.

Agents must be managed as software, with security, governance, and technical controls, while recognizing that their probabilistic behavior requires additional supervision. “You have to do both,” Tatsiy says.

An intern does not receive every password and authorization on the first morning. Neither should an agent. It has to be onboarded, trained, watched, retrained when its environment changes, and eventually retired when it’s obsolete.

Quadient generally avoids giving agents human names. Tatsiy worries that doing so encourages people to see them as real colleagues capable of judgment.

“Overtrust is a problem,” she says, particularly because AI tends to state conclusions with such assurance. Tatsiy prefers using AI to challenge a hypothesis or surface questions people haven’t considered rather than allowing it to make the conclusion for them.

Yet the intern analogy has proved useful operationally.

In one accounts-payable deployment, Quadient had an agent effectively shadow human workers and learn from their feedback across different scenarios. Only after its performance had been tested across increasing levels of complexity did it begin providing recommendations. Humans still made the final decision.

The agent, in other words, earned more trust. It didn’t earn accountability.

Borrow from HR, but keep humans in charge

The emerging divide may therefore be less about whether agents are employees or software than about which parts of each management model IT leaders should retain.

Agents need individual technical identities, but not human identities. They need defined responsibilities, but not jobs in the human sense. They need monitoring and evaluation, but not annual performance reviews. They can receive progressively greater authority, but not progressively greater accountability.

And the stronger they become, the more important that distinction becomes.

Treating an autonomous agent like ordinary SaaS ignores the fact that it can interpret, choose, and act in ways conventional software cannot. Treating it like Steve from Accounting creates a different problem: sooner or later somebody may start assuming “Steve” knows what he’s doing.

IT leaders may indeed need to borrow heavily from HR as agentic AI spreads across the enterprise. They just shouldn’t start issuing employee badges.

Related reading:

• 6 ways to make AI accountability stick
• From chatbots to colleagues: How agentic AI is redefining enterprise automation
• Why AI adoption keeps outrunning governance — and what to do about it
• AI is spreading decision-making, but not accountability

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. 

Every cloud architect, every financial services executive, and frankly every enterprise CTO should be paying attention to what is going on in the United Kingdom right now. Britain has formally designated Microsoft, Google, Amazon Web Services, and Oracle as “critical third parties” to the financial sector, meaning they are now subject to direct oversight by UK financial regulators rather than being treated as distant infrastructure suppliers outside the regulatory perimeter.

The designations took effect on July 13, 2026, and the oversight will be carried out by the Bank of England, the Prudential Regulation Authority, and the Financial Conduct Authority. The point is straightforward. If these platforms underpin critical banking and financial operations, then their resilience is no longer just a procurement issue. It is now a matter of financial stability.

This is a bigger deal than many people understand. For years, enterprises have treated cloud platforms as outsourced technology providers. Regulators are now making a different argument. They are saying that when enough banks, insurers, payment firms, and market infrastructures depend on the same small group of cloud providers, those providers become systemic infrastructure whether they like the label or not. Reuters reported that the UK’s framework will bring requirements such as resilience testing, self-assessments, and incident reporting directly to these providers. In other words, this is not just more policy language. It is operational oversight aimed at the platforms themselves.

The first thing to understand is that this move is not anti-cloud. It is the opposite. It is an acknowledgment that cloud has become too important to remain lightly touched when it comes to systemic risk. Regulators are not trying to unwind cloud adoption in financial services. They are accepting the reality that core financial operations now depend on a concentrated set of external platforms. When that happens, the conversation changes from vendor management to infrastructure resilience.

Systemic cloud concentration

The market has spent years talking about cloud concentration risk as if it were some vague future concern. It is no longer abstract. The UK is acting because concentration at this level means that a disruption at one major provider can ripple across multiple institutions simultaneously. That is the core issue. It is not whether Microsoft, Google, Amazon, or Oracle are competent operators. In many cases, they are exceptionally good at what they do. The problem is that too much critical activity depends on too few shared platforms.

This is a concept many business and technology leaders still struggle to internalize. A single bank can do a fine job managing its own vendor exposure and still be part of a broader systemic vulnerability if every other bank is relying on the same provider, the same region, the same identity layer, or the same operational dependencies. Regulators are stepping in because the risk is collective, not just institutional. That is why this matters well beyond the UK. It provides a framework for thinking about cloud not as outsourced capacity, but as part of the plumbing of the financial system itself.

Regulators expand their reach

Traditionally, regulators focused on the bank, insurer, or market institution and expected that entity to manage the risks created by outside suppliers. That approach still exists, and financial institutions remain accountable for their own resilience. However, the UK now says that, in some cases, this indirect model is not enough. If a third party becomes critical enough, regulators want direct line of sight into that provider’s resilience posture as well. This is probably the most important architectural and policy signal in the entire move.

That changes the relationship between the financial sector and the cloud providers in a meaningful way. Direct oversight means cloud providers are increasingly being treated less like optional technology partners and more like essential utilities that support national economic confidence. Once that happens, architecture decisions start to carry a different weight. Decisions about control planes, failover models, identity dependencies, regional design, observability, and incident response are no longer just internal engineering choices. They become part of a broader resilience conversation that may now include regulators.

A lot of enterprises have been behind the curve. They built architectures under the assumption that the cloud was simply a faster and cheaper hosting model. It is not. At scale, cloud is shared critical infrastructure. The UK has now made that point explicit.

More scrutiny isn’t more safety

It’s tempting to read this and assume the problem has now been solved. It has not. Direct oversight of hyperscalers does not remove the responsibility of banks or other enterprises to architect well. In fact, it should push them to architect better. A regulated provider can still be a concentration point. A resilient platform can still be used in a fragile way. And a well-run cloud service can still become part of a badly designed dependency chain.

If you are in a regulated or highly sensitive industry, you should take this as a warning that resilience can no longer be treated as a side topic. You need to know exactly which services are business-critical, which control planes are shared, which identity systems create cross-platform dependencies, and how your recovery assumptions actually work in the real world. Most organizations are much weaker here than they believe.

The UK’s move should also force a hard conversation about operational transparency. If regulators are requiring incident reporting and resilience evidence from major providers, enterprises should be demanding clearer dependency mapping and better architectural visibility inside their own environments. You cannot build true resilience on top of assumptions and vendor slide decks.

The start of a much larger shift

The deeper message is that cloud is crossing a line from enterprise technology choice into national and sector-level infrastructure policy. Once a few governments and central regulators start treating cloud this way, the rest of the world tends to follow in some form. We have already seen comparable concern in Europe, and now the UK has made its own move with direct designations and direct oversight.

Architects, CIOs, and boards need to mature their thinking quickly. The conversation is no longer limited to feature depth, discounts, migration speed, or which provider has the best AI story this quarter. The conversation is now about resilience, concentration, transparency, control, and systemic dependency. Those are bigger issues, and they are not going away.

The UK is telling the market something very clearly. Microsoft, Google, Amazon, and Oracle are not just selling cloud services into the financial sector. They are becoming part of the infrastructure on which that sector depends. Once you understand that, everything changes, including how we architect for the future.

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.

China has created an international organization to set standards and introduce regulation for AI, inviting 28 other countries to join — but the US, a leading AI powerhouse is not part it.

The World Artificial Intelligence Cooperation Organization (WAICO) was established by 29 countries, including China, Russia and Brazil, at a ceremony in Shanghai, China, on July 16. Notably absent are the US, the European Union and its member states, the UK, Japan and South Korea.

Chinese AI companies have made a concerted effort to provide an alternative to US dominance. While the US is clearly ahead, Chinese enterprises are looking to narrow the gap in various areas: the open-weight model market, AI cyber protection and open source AI.

WAICO has been some years in development and has been designed to set some universal guidelines in AI. Researchers say WAICO differs in three ways from other initiatives to create global AI organizations: membership open to any sovereign state, there is no regime-type test for entry, and its agenda is built around development and the global capability divide.

The signing ceremony to create WAICO comes just days after Demis Hassabis, CEO of Google DeepMind, called on the US to take a lead in global AI regulation. “The US is well-positioned to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives,” Hassabis wrote.

Enterprises implementing agentic AI face a challenge: Which tools should they allow their agents to use, where can they be found, and how can they be used safely? A new protocol, Agentic Resource Discovery, or ARD, aims to let agents answer those questions for themselves. Behind it are Google, Microsoft, Cisco, Nvidia, Salesforce and others.

ARD aims to standardize the way that tools and services are shared across systems within a corporate domain. For example, when investigating a production problem, an agent may want to query engineering documentation and open support tickets, deployment history and observability systems, all of which could be managed by different registries and across different silos. There is no common layer that pulls them together. ARD has been designed to be that layer.

It operates across two levels. Catalogs and Registries. In the first, an organization publishes a catalog setting out its available capabilities. The Registries layer act as a form of search engine, crawling those published catalogs.

The ARD specification is available now. Organizations are invited to publish their own catalogs using the quickstart guide. After this, they are able to join the community and participate in the evolution of ARD.

This article first appeared on InfoWorld.

Enterprises implementing agentic AI face a challenge: Which tools should they allow their agents to use, where can they be found, and how can they be used safely? A new protocol, Agentic Resource Discovery, or ARD, aims to let agents answer those questions for themselves. Behind it are Google, Microsoft, Cisco, Nvidia, Salesforce and others.

ARD aims to standardize the way that tools and services are shared across systems within a corporate domain. For example, when investigating a production problem, an agent may want to query engineering documentation and open support tickets, deployment history and observability systems, all of which could be managed by different registries and across different silos. There is no common layer that pulls them together. ARD has been designed to be that layer.

It operates across two levels. Catalogs and Registries. In the first, an organization publishes a catalog setting out its available capabilities. The Registries layer act as a form of search engine, crawling those published catalogs.

The ARD specification is available now. Organizations are invited to publish their own catalogs using the quickstart guide. After this, they are able to join the community and participate in the evolution of ARD.

Google, Microsoft, OpenAI, and others want to help enterprises demonstrate that their AI applications are behaving themselves through the creation of a new foundation.

The Appia Foundation will, it explained rather impenetrably, “establish modular specifications that provide a connecting layer to bridge foundational global standards with practical, trusted assessments across the global AI value chain.”

Those specifications will help AI users ascertain whether the systems they are using meet all the obligations that apply to them in the form of standards and regulations, it said. It’s a challenging task with so much regional variation in requirements, and where the EU, for example, is more tightly controlled than the US.

The Foundation has established a set of criteria to demonstrate conformity with what is expected. There are two layers: the Requirements and Guidance layers will help users determine what is actually required, while the Assessment Enablement layer will look at how those requirements are evaluated.

Appia stressed that what it is offering are not standards — which are set by recognized international bodies such as ISO/IEC — but a means of assessing what those standards mean and how they can be used by organizations. However, the Foundation said that some of the criteria that it is introducing may become standards themselves after a period of time.

The Appia Foundation is hosted by the Linux Foundation’s Joint Development Foundation, and its other members include Arm, Ericsson, Mastercard, Mitsubishi Electric, Omron, Schneider Electric, and Siemens. It is also looking to bring academics and government into the fold, so that it can establish an advisory board.

This article first appeared on CIO.

Google, Microsoft, OpenAI, and others want to help enterprises demonstrate that their AI applications are behaving themselves through the creation of a new foundation.

The Appia Foundation will, it explained rather impenetrably, “establish modular specifications that provide a connecting layer to bridge foundational global standards with practical, trusted assessments across the global AI value chain.”

Those specifications will help AI users ascertain whether the systems they are using meet all the obligations that apply to them in the form of standards and regulations, it said. It’s a challenging task with so much regional variation in requirements, and where the EU, for example, is more tightly controlled than the US.

The Foundation has established a set of criteria to demonstrate conformity with what is expected. There are two layers: the Requirements and Guidance layers will help users determine what is actually required, while the Assessment Enablement layer will look at how those requirements are evaluated.

Appia stressed that what it is offering are not standards — which are set by recognized international bodies such as ISO/IEC — but a means of assessing what those standards mean and how they can be used by organizations. However, the Foundation said that some of the criteria that it is introducing may become standards themselves after a period of time.

The Appia Foundation is hosted by the Linux Foundation’s Joint Development Foundation, and its other members include Arm, Ericsson, Mastercard, Mitsubishi Electric, Omron, Schneider Electric, and Siemens. It is also looking to bring academics and government into the fold, so that it can establish an advisory board.

This article first appeared on CIO.