API
Application programming interface: a defined way for one piece of software to ask another to do something. Having APIs does not make a business agent-ready; the actions must be described precisely.
An API is a defined contract that lets one program request data or actions from another: send these inputs to this address, receive this output. APIs are how modern software systems connect, and how developers use AI models, which are themselves usually reached through an API.
"We have APIs" is not an answer
When organizations think about AI agents, a common reply is that they already have APIs. That is not the same as being ready. An API built for developers may not say what an action means, what it changes, which permissions it needs, what it does on partial failure or what to do when the situation does not fit the happy path. A human integrator fills those gaps from experience; an agent cannot.
An agent-ready interface describes the action with much greater precision. See callable surface and capability readiness. Standards such as the Model Context Protocol help present these actions consistently to agents.
Read more in The next AI interface may never be seen.
Related terms
Callable surface
The set of actions an organization exposes so AI agents can invoke them directly, each described precisely enough to be agent-ready: meaning, inputs, permissions, side effects and recovery.
Model Context Protocol
An open standard, usually called MCP, for connecting AI models and agents to external tools and data sources in a consistent way, so they can discover and call an organization's capabilities.
Capability readiness
How ready an organization's actions are to be used by AI agents: whether each action is a real business capability, exposed cleanly, with clear permissions, side effects and recovery.
Used in these essays
OpenAI Decisions API turns probability into policy
The provocative part of OpenAI's Decisions API is not that AI can make choices. It is that a model score can quietly become an action — and an action can quietly become policy.
A cheaper AI model can move the cost instead of removing it
A lower model bill can hide a higher workflow bill. The useful AI TCO question is not only what got cheaper, but where the cost moved.
The first AI incident report should be incomplete
OpenAI's new misalignment disclosure framework exposes a useful enterprise design principle: record anomalous AI behavior before the organization has finished explaining it. Otherwise incident systems quietly become filters for what teams already understand.
The next AI interface may never be seen
Agents are turning software capabilities into an interface of their own. The next enterprise design problem is deciding what should be callable, by whom, and under which boundaries.
The benchmark needs a runtime address
AI systems are increasingly dynamic at runtime. Enterprise evaluation should qualify the serving route, harness, tools and fallback conditions, not just the model name.