Remember the childhood game of Telephone? The outcome is predictably unpredictable.
The message starts clearly enough. But with each handoff, small changes accumulate.
A word gets misheard.
Someone fills in a gap.
By the time the message reaches the final person, it may barely resemble the original.
For a long time, enterprise software tolerated a version of this dynamic.
Systems exchanged information through APIs but humans remained close enough to the process to absorb inconsistency. If a field changed from “name” to “first_name,” if a header moved, or if documentation lagged behind implementation, developers usually figured it out contextually. They checked logs, compared contracts, made assumptions, and corrected the course.
Today, however, the consumer on the other end is increasingly no longer a human.
Already 89% of developers use AI, yet only 24% report actively designing APIs with AI agents in mind.
This gap creates a new kind of operational risk: organizations are introducing machine consumers into environments largely designed around human flexibility.
AI agents do not interact with APIs the way humans do.
As Axian Solution Architect Justin Hart put it in a recent interview, “AI agents depend on predictability.” They discover contracts, infer behavior, generate code, and execute workflows based on the interfaces they are given. When those interfaces change unexpectedly, or drift away from what they promise, the effects can propagate immediately downstream.
APIs: No Longer Just Plumbing
This is changing the role APIs play inside organizations. As Hart frames the shift:
“APIs are no longer integration plumbing. They are operational infrastructure.”
For years, APIs were treated primarily as integration infrastructure. Their job was to connect systems, move information, and provide flexibility between applications. As long as requests succeeded and consumers remained reasonably satisfied, the details of governance often stayed in the background.
This model worked partly because the consumers on the other side were usually people.
Developers could interpret inconsistencies. If an API version changed unexpectedly, if documentation lagged behind implementation, or if a parameter behaved differently than expected, teams could investigate and adapt. Human consumers introduced a layer of resilience because they were capable of debugging the contract itself.
The introduction of AI agents into the loop has changed that relationship.
Increasingly, agents are consuming APIs directly to retrieve information, generate code, execute workflows, and interact with enterprise systems. In practical terms, APIs are becoming less like plumbing hidden behind the walls and more like operational infrastructure coordinating traffic across an organization.
This distinction matters because AI agents depend heavily on contracts.
A contract does more than describe what data exists. It defines the expectations that govern the system, including:
- What a request should look like
- What fields should exist
- How authentication works
- What responses should return
- What behaviors consumers can safely rely on
Even seemingly small adjustments can have outsized consequences. A renamed parameter, a modified request structure, a changed header, or an updated response body may be obvious to a human reviewer. However, to an automated consumer, these changes can represent a broken agreement.
Machines consume literally, while humans consume interpretively. This difference becomes visible the moment systems begin to drift.
The Quiet Problem Called Drift
Most organizations do not intentionally break their APIs. Drift usually appears through normal developmental activity. Teams improve naming conventions, reorganize schemas, update request structures, introduce new authentication requirements, retire legacy fields, or release new versions intended to improve usability. In isolation, each change may seem minor.
The challenge emerges when those changes accumulate faster than the contracts surrounding them.
Consider a simple example. An API originally expects a field called “name.” Later, a team decides it should become “first_name” to support additional structure. From a human perspective, the change feels obvious. Most developers encountering an unexpected response can compare versions, inspect logs, infer intent, and adjust their implementation.
AI agents approach the problem differently.
Agents increasingly discover APIs dynamically, interpret contracts, generate integrations, and execute tasks based on what those interfaces promise. When the contract changes unexpectedly, the agent does not necessarily understand the intent behind the change. It sees that the expected structure no longer exists and attempts to infer what happened.
Sometimes it guesses correctly. Sometimes not.
That uncertainty becomes risk when the agent is no longer just suggesting an action, but executing one across connected systems.
Drift rarely announces itself as a major architectural failure. More often, it appears as small operational symptoms, such as:
- Unexpected outputs
- Broken workflows
- Production support tickets
- Inconsistent behavior between versions
- Systems quietly degrading over time
This is why contract discipline becomes increasingly important in AI environments. Backward compatibility, versioning, deprecation periods, and clear publication practices are no longer administrative overhead. They have become mechanisms for preserving predictability while systems continue to evolve.
Drift is not usually a failure. It is unmanaged change becoming visible. Good governance must take this into account.
How Governance Becomes an AI Problem
API governance has traditionally been discussed as a technical concern. Its purpose was to improve interoperability, support version management, maintain standards, and reduce integration friction across systems. For most organizations, governance existed to help teams move faster without losing control.
AI changes the consequences of getting it wrong.
What used to create integration friction can now create operational exposure.
Last year, 55% of organizations delayed rolling out new applications because of API security concerns. This statistic warrants attention but not because it points to catastrophic failures. Rather, it highlights something subtler: weak governance increasingly creates operational drag.
Hart emphasizes that these issues rarely emerge as dramatic outages. More often, they surface as production support tickets, emergency fixes, and engineering effort redirected away from delivery. A small change in a contract may appear harmless at the point of implementation but create downstream instability once agents begin consuming those interfaces at scale.
This changes the role governance plays.
As Hart puts it, “We have to make sure that we handle that gracefully.”
In AI environments, change itself is not the problem. Systems need to evolve. APIs need to improve. The challenge is creating mechanisms that allow consumers, both human and machine, to adapt without interruption.
Practices such as schema-first design, version interoperability, deprecation windows, publication standards, and observability become less about process discipline and more about preserving predictability.
API governance is no longer back-office technical hygiene. It is what makes AI-enabled systems safe to scale.
The cost of weak governance rarely appears where and when the change happened. But it does usually appear somewhere downstream, which raises a practical question: “What does governance look like when your consumers never open support tickets?”
Building APIs for Consumers Who Never Call Support
Organizations cannot fully control how AI agents behave, but they can control the environments those agents consume. This distinction becomes increasingly important as AI systems move from experimentation into operational workflows. Humans encountering unclear interfaces often ask questions, search documentation, escalate issues, or improvise around inconsistencies.
Automated consumers do not behave that way. They continue operating according to the information and contracts available to them. Hart summarizes the goal here simply: “We want to remove that entropy.”
And that objective begins with contract discipline.
Contracts should be treated as products rather than implementation details. Teams need:
- Clear ownership
- Intentional publication practices
- Version visibility
- Deliberate deprecation processes
Consumers should understand not only what exists today but what may change tomorrow. Observability becomes equally important. Organizations increasingly need visibility into how APIs are being consumed, which versions remain active, where failures occur, and whether unexpected behavior appears after changes are introduced. Drift cannot be managed if it cannot be observed in the first place.
Security and access controls also become more important in machine-consumed environments. AI agents should not receive special treatment or bypass governance because they appear adaptive. Authentication, authorization, and usage controls need to remain consistent regardless of whether the consumer is a person, an application, or an autonomous workflow.
Ultimately, good governance is becoming less about restricting movement and more about making change understandable.
Predictability does not eliminate innovation; it creates conditions where innovation can scale. With that focus recentered, organizations are no longer building APIs for consumers who open support tickets. Increasingly, they are building for consumers who simply keep going in silence regardless of the contract mismatches they encounter.
Why AI Scaling Has Become an Infrastructure Problem
For much of the recent AI cycle, organizations treated scale as a capability problem. The assumption was straightforward: as models improve and generate more useful outputs, enterprise adoption naturally follows.
That has not entirely happened.
Only about one-third of organizations report successfully scaling AI across the enterprise despite widespread investment and experimentation. This finding suggests many organizations are encountering a different constraint than expected.
Generating software, creating workflows, and connecting systems have become dramatically easier. Moving those outcomes into environments that can support reliability, ownership, governance, observability, and long-term operation remains considerably harder.
The pattern should feel familiar.
Throughout our conversation, Hart repeatedly returned to the idea that AI changes the importance of contracts rather than eliminating them. Intelligence does not reduce the need for operational discipline. In many cases, it increases sensitivity to weaknesses that already existed.
As organizations introduce more autonomous consumers into production environments, infrastructure becomes more visible. Interfaces, contracts, versioning strategies, and governance practices increasingly determine whether systems continue operating predictably at scale, ultimately changing what organizations should optimize for.
The question is becoming less “How intelligent is the system?” and increasingly “How reliably can the environment absorb that intelligence?”
Stop Teaching Systems to Guess
The childhood game of Telephone works because people instinctively compensate for ambiguity. Participants repeat what they think they heard, fill in missing pieces, and unconsciously repair inconsistencies along the way. The outcome may be distortion, but the process depends on interpretation.
Enterprise systems historically worked in a similar way.
Developers absorbed ambiguity. They checked documentation, compared outputs, interpreted intent, and corrected small inconsistencies before they became larger failures. Human flexibility allowed systems to tolerate a surprising amount of imperfection. AI agents change this process.
Automated consumers do not ask whether a response looks unusual or whether a contract changed unexpectedly. They continue operating according to the information available to them and scale whatever assumptions they make across workflows, environments, and downstream systems.
Not because AI systems are inherently fragile. But rather, because intelligence should not be mistaken for understanding.
Hart returns repeatedly to a simple objective: reduce entropy. Stable contracts, visible change management, observability, and predictable interfaces are not governance theater. They are mechanisms for helping increasingly autonomous systems continue operating in environments that never stop evolving.
Air traffic control (ATC) offers a useful comparison here. The purpose of ATC is not to prevent movement; it is to make movement predictable enough to scale safely. API governance increasingly serves the same function.
APIs used to be agreements between teams of human users. Now, they are becoming agreements between systems comprising both human and machine consumers.
As organizations hand more work to AI agents, the highest-performing systems may not be the most strictly intelligent so much as they’ll simply be the ones that make misunderstanding impossible.
Building Systems That Stay Understandable
As organizations move from experimentation to operational AI, the challenge is becoming less about generating capability and more about sustaining reliability. Models will continue to improve. Interfaces, contracts, governance, and operational discipline will increasingly determine whether those improvements translate into durable outcomes.
Organizations do not necessarily need more intelligent systems. They need environments where intelligence can operate predictably. Achieving that end requires three things:
- Creating APIs that evolve without introducing unnecessary entropy
- Governance practices that support movement rather than restrict it
- Delivery environments that remain understandable even as autonomous consumers become more common
As AI agents begin consuming more enterprise systems, API governance becomes part of the infrastructure for scale.
Axian helps organizations design the contracts, controls, observability, and governance practices that keep AI-enabled systems understandable as they evolve.
If your teams are preparing APIs for more autonomous consumers, talk with Axian about building the governance foundation to support reliable scale.