From Endpoints to Intentions: Preparing APIs for Autonomous Clients
It is likely your API’s next consumer won’t be human. Are you ready?
In my latest whitepaper (for Treblle) I dig into a shift that’s reshaping how we think about API design.
The 10-page paper ("Preparing Your APIs for AI Agents") was written to accompany a webinar hosted by Treblle, where I was invited as a guest speaker alongside Vedran Cindrić (Treblle’s CEO) and Harsha Chelle (Treblle’s API Strategist).
Together, we explored what it takes to rethink API design for the emerging world of AI-driven clients, bots, and autonomous systems.
A key point covered both in the webinar and the paper is that APIs are no longer just for humans. The rise of AI-powered clients like autonomous agents, LLMs, and orchestration tools means your APIs must support new ways of being seen, understood, and used.
And machines do not think like human developers. They do not rely on portals, forums, or documentation. Instead, they scan API responses and metadata for signals: intent, context, discoverability, and predictability.
Your Endpoints Are Not Enough
In a world where AI-powered clients are increasingly common, simply exposing operational endpoints is no longer sufficient.
Autonomous agents approach APIs differently: they scan responses for patterns in field names, infer meaning from the structure of the data, and search for actionable affordance. Machines seek out signals that reveal how they can create, update, delete, or navigate resources.
If those affordances and semantic cues are missing, machines will struggle to use your APIs effectively, no matter how technically correct your implementations are.
Machines are bad at guessing.
If your API does not explicitly describe available actions and transitions, machines will not infer them safely. They need clear declarations of intent, not just raw endpoints.
Machines do not ask for help.
Unlike human developers who can troubleshoot or escalate issues, autonomous clients fail silently. They depend on machine-readable context and predictable structures embedded in your API responses.
Machines still expect to succeed.
Whether powered by orchestration platforms, LLMs, or domain-specific bots, machine clients are tasked with achieving goals efficiently and reliably. They will favor APIs that are easy to navigate, semantically rich, and resilient in the face of evolving conditions.
Without these design considerations, your API risks being invisible, or worse, unusable, to the next wave of API consumers.
Five Shifts Toward Machine-Ready APIs
In the whitepaper, I lay out five essential shifts (and how you can make them right now) that help make your APIs machine-ready.
Shift from Interfaces to Intentions
Help machines understand what your API does and why it matters.
Make Context Machine-Readable
Metadata is the new interface. Declare purpose, ownership, and domain.
Standardize Interactions
Predictability reduces risk. Avoid surprises in common patterns.
Enable Discovery Through Ecosystem Signals
If machines cannot find your API, they cannot use it.
Observe, Adapt, and Iterate
Track how bots behave. Learn from their interactions. Evolve your APIs over time.
These shifts are influenced by the experience and writings of system design legends like Donald Norman, Douglas Engelbart, Roy Fielding, Ted Nelson, and Christopher Alexander—each of whom understood that good systems are not just built; they are grown.
Why Now?
Because autonomous clients are already here. They are scanning OpenAPI specs, executing plans, and calling APIs—all without human guidance.
This is the new baseline. If your APIs are not designed with this in mind, you are missing the next generation of consumption.
If you are building APIs or thinking about how AI will consume them, this paper is for you.