You May Have OpenAPI, But Is It AI-Ready?

As AI agents move from experiments into real-world automation, it’s becoming clear that valid OpenAPI descriptions alone are not enough. AI systems don’t just read API descriptions, they must interpret, reason, and act on them reliably. Unfortunately, many APIs that work fine for human developers fall short when consumed by autonomous agents.

In this video with Frank Kilcommins, he introduces a practical framework for scoring API readiness for AI consumption. Instead of treating AI readiness as a vague aspiration, this model breaks it down into measurable dimensions. These cover structural compliance, developer clarity, semantic intent, agent usability, security, and discoverability.

What makes this especially relevant is that AI-based consumers don’t fail in the same way humans do. Where human developers can compensate for gaps in documentation or semantics through trial, error, and informal knowledge, agents either stop or make confident but incorrect assumptions. That changes the risk profile of API design in subtle but important ways, particularly once APIs are exposed directly to autonomous systems operating at scale. Another key takeaway is that AI readiness isn’t a binary property of an API. It’s unevenly distributed across an organization’s API landscape. Some APIs may be structurally sound but semantically weak. Others may be well-documented for humans but unpredictable for agents. Scoring creates a shared language for these differences, making it easier to prioritize improvements and address systemic issues rather than debating individual design choices in isolation.

Finally, the broader implication is that API design is entering a more context-driven phase. As agents increasingly reason about workflows, intent, and outcomes rather than individual endpoints, the quality and clarity of the signals embedded in API descriptions matter more than ever. Measuring AI readiness is less about chasing a new standard and more about aligning APIs with how they are actually being discovered, understood, and used in an agent-driven world.