The vocabulary of API management in the agentic era

The layer that decides what happens to an organization's APIs: which ones exist, who owns them, what each audience is allowed to see, and how they reach the developers, applications, and agents that consume them. It sits above the gateways and runtimes rather than beside them.

The distinction is control plane against data plane. A gateway is data plane: it moves and polices individual requests while they are in flight. A control plane never touches that traffic, it governs the surface the traffic runs against. Elva is an example of an API control plane built for AI agents as well as developers.

A named, versioned view of your API for one consumer: which endpoints they see, which fields exist for them, how they authenticate, and what happens when something they depend on changes. Contracts are machine-checked on every commit.

One API usually needs several contracts. A mobile client, a partner integration, an internal service, and an AI agent each need a different subset of the same surface.

A change to an API that invalidates something a consumer already depends on: a removed endpoint or field, a narrowed type, a new required parameter, or a changed auth requirement. Whether a change breaks is a property of the contract, not of the diff.

The same commit can be breaking for one consumer and invisible to another, which is why the check runs per contract rather than once per repo.

The gap that opens between what an API specification claims and what the running code actually does. Drift accumulates whenever an endpoint changes and the hand-maintained spec does not.

Drift is why specs generated from code behave differently from specs maintained beside it: a generated spec cannot silently fall out of date, because it is rebuilt from the source of truth on every commit.

An open standard that defines how AI clients such as Claude, Cursor, and ChatGPT discover what a server offers and call its tools. It gives models a uniform way to reach external systems without a bespoke integration per client.

A server that exposes a set of tools to AI clients over the Model Context Protocol. For an API team it is a thin, curated layer in front of the API you already run, not a replacement for it.

Around 15 to 25 well-described tools beat an endpoint dump, because the model picks tools by reading their descriptions rather than by reading your API.

The hosted layer agents connect to instead of reaching an MCP server directly. Every tool call passes five checks in order: identity, tool scope, field redaction per contract, rate and quota, and an audit record with actor, tool, arguments hash, latency, and outcome.

An MCP server whose tools are derived from an API contract rather than hand-written, and whose every tool call passes identity, scope, field redaction, rate, and audit checks before it reaches the upstream API. Governance is a property of the running server, not of the code someone wrote once.

Three tiers get conflated constantly. An SDK writes server code and governs nothing on its own; a gateway adds auth and rate limits in front of a server someone already wrote; a platform generates the server from the API and governs it from the same source.

The controls that decide whether an agent's tool call reaches a production API: which agent is calling, which tools its key is scoped to, which fields are redacted before the response leaves, what rate it is held to, and what the audit log records. A tool call is a production request, so it warrants the same controls as any other production traffic.

Security posture and compliance certification are separate questions. Scoping, redaction, revocation, and audit logging are properties of how a server is deployed; SOC 2 or GDPR status is a property of the vendor. Ask about both, and do not let a badge stand in for an answer about scopes.

An in-product chat where an AI agent is pointed at your own MCP server so you can watch every tool call it makes: the arguments it sent, the response it got, the latency, and whether the gateway allowed the call.

It answers the question a tool list cannot. A server can expose the right tools and still fail, because the agent cannot tell which one to pick. Playground calls do not count against a plan’s tool-call quota.

Structured reports filed by the AI agents that called an MCP server, saying why a tool was hard to use: the description did not explain when to use it, it asked for too many parameters, or the server timed out. Each item is attached to the endpoint it came from.

It is not an error log. A log records that a call failed and with which status; agent feedback records that the interface was unusable, in the words of the client that gave up. A tool can return 200 on every call and still be unusable.

One request from an agent or client through a hosted MCP server, the agentic equivalent of an API request, and the unit MCP usage is measured in.

A tool call is a production request against a real system, so it deserves the same authentication, authorization, rate limiting, and logging as any other production traffic.

A per-endpoint grade for whether an API is usable by an AI agent, scored across design, developer experience, AI readiness, security, and performance, with a concrete fix attached to each finding.

It answers a different question from spec linting. A spec can be valid and lint-clean while still being unusable by an agent: no descriptions, loose types, undocumented auth.

The rules and automated checks that decide what an API is allowed to expose, to whom, and how it is allowed to change, enforced in the pipeline rather than in review comments.

Testing that verifies an API still satisfies what its consumers depend on, rather than only that its endpoints respond. A contract test fails when a change breaks a specific consumer, even if the endpoint is healthy.

A generated set of hostile requests that probe how an API behaves under the inputs agents actually produce: wrong types, truncated JSON, extra fields, and nulls where a string is required.

It exists because agents fail differently from humans. An agent that half-understands your schema will send plausible-looking nonsense at full request rate.

The standard machine-readable description of a REST API: its endpoints, parameters, request and response schemas, and authentication. It is the format most API tooling reads and writes.

A spec is where an API pipeline starts, not where it ends. Having one says nothing about whether the API is documented, tested, governed, or safe to expose.