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Most APIs do not need a rebuild before an AI agent can call them. They do need an audit of the parts a human developer could quietly work around: the operation names and descriptions the agent reads, the size of the responses it receives, whether errors tell it what to do next, what happens when a write is retried, and which credential it uses. The core of a well-run API, meaning its resources, HTTP semantics and authentication, usually survives. What fails is the assumption that the caller reads the documentation once, writes deterministic code, and stops when something goes wrong. An agent chooses operations at runtime from their descriptions, reasons over everything a response contains, chains several calls together, and may retry on its own.
How an agent caller differs from a developer
A developer who integrates your API makes decisions once, at build time, and the code repeats them. An agent makes those decisions again on every turn. The table below sets out where that changes what your API has to do.
| Concern | Human developer | AI agent |
|---|---|---|
| How the API is learned | Reads the documentation once, then hardcodes the calls it needs | Selects operations at runtime from names and descriptions |
| What the contract is for | A reference to write code against | A live input to reasoning, read again on each decision |
| Response handling | Parses the fields the code expects and ignores the rest | Reads the whole body; unusual or verbose output enters the next decision |
| Retries | Written deliberately, with known behavior | May retry after a timeout or an ambiguous error, without knowing whether the first write succeeded |
| Sequencing | Fixed in code | Chosen dynamically; a wrong read can steer later writes |
| Credentials | Usually the developer’s own key for one application | Often delegated from a user, or shared across several tools |
| Data volume | The developer decides how much to fetch | Large responses consume the agent’s context window and can raise token cost |
| Errors | Logged and fixed in code later | Must be interpreted immediately to choose the next action |
Where the API usually breaks first
Most failures trace back to one of the differences above. These are the patterns to look for:
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- Retries repeat writes. A timeout leaves the outcome unknown. A retry without a stable identifier for the action can create a second payment, a second shipment, or a second deletion attempt.
- Responses flood the context. An unpaginated list or a record with large embedded fields can fill the agent’s context and make its later reasoning worse.
- Errors give no direction. A generic failure message forces the agent to guess whether to correct the input, wait, or stop.
- Credentials are broader than the task. An agent holding a user’s full token can do anything that user can do, including things the task never needed.
- Returned text steers actions. A support ticket or web page returned by a tool may contain instructions, and the agent may treat them as requests.
- Many callers hit the same limit at once. Retry loops from several agents amplify load on one endpoint.
Rebuild or improve?
In most cases, keep the existing API and change three layers. The first is the contract: operation names, descriptions, schemas and error bodies. The second is the server-side controls: page limits, scopes, idempotency handling and confirmation for consequential actions. The third is what the agent is allowed to see, which may be a smaller set of task-oriented operations rather than every endpoint.
A rebuild is justified mainly when the underlying model is inconsistent. For example, if the same identifier means different things on different endpoints, no amount of description will make the agent’s choices reliable. A task-specific layer over the main API is a reasonable middle path, but it adds something to maintain and can drift from the main API unless the two are versioned together.
A practical sequence for preparing the API
1. Inspect the contract the agent actually receives
Describe the API in a machine-readable format such as OpenAPI. If a tool layer exposes the API to agents, inspect the generated tool surface as well, because that is what the agent reads: the operation name, the description, and the input schema. Check that the following are consistent across operations:
- Operation names and verbs, so that one action has one name.
- Identifier formats, so an ID from one call is accepted by the next.
- Resource state values, such as “pending” and “processing”, and what each one means.
- Pagination parameters and their defaults.
- Authentication conventions and the error structure.
Each operation description should say what the operation does and what it changes, in short, unambiguous sentences. The IETF draft discussed below treats descriptions and returned data as inputs to the agent’s reasoning, so the wording is part of the interface.
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Set response limits on the server, not in the client. Use cursor-based pagination with a maximum page size enforced by the API, and reject oversized requests with a clear message rather than trusting the caller to ask for less. For large records, return a summary and a separate operation to fetch detail by identifier.
Errors should say whether the request can be corrected or retried. A structure like the following, shown here as an illustrative shape rather than a standard schema, gives the agent something to act on:
{
"error": {
"code": "rate_limited",
"message": "Too many requests for this tool in the current window.",
"retryable": true,
"retry_after_seconds": 30,
"action": "wait_then_retry_same_request"
}
}A validation failure would instead return "retryable": false with "action": "correct_input" and the name of the offending field. For rate limits, the standard HTTP Retry-After header carries the same signal in a form that clients and proxies already understand.
For writes, the central protection is idempotency. The agent or the client supplies a key that belongs to the action rather than to the attempt. The usual sequence works like this:
- The agent creates one key for the logical action, for example “refund order 4471”, and stores it with its task state.
- The server records the key together with the request and the outcome.
- The request times out, so the agent does not know whether the refund happened.
- The agent retries with the same key. The server finds the stored key and returns the original result instead of acting again.
- If the agent generates a new key on retry, the server treats the call as a new refund. Keys must be tied to the action, not regenerated per attempt.
Where the domain allows it, add a preview or dry-run mode that returns what would change, and an undo path for reversible actions. For irreversible or high-impact actions, require a confirmation step that shows the agent’s planned change to a person or to a separate approval process before the write is applied.
3. Separate caller identity from delegated authority
Decide, for each workflow, which of two models applies:
- Machine-to-machine: the agent runs under its own service identity. This suits scheduled jobs and organisation-wide tasks. Permissions belong to the service, so review them the way you would review any service account.
- User-delegated: the agent acts for a named person. This suits tasks that should see only that person’s data. The token should be issued for the task and be narrower than the person’s full access.
Whichever model applies, do not pass one caller’s bearer token through the agent system to every downstream tool. Give each tool or server its own token scoped to the task and resource it needs, and have the API check every request against that scope. Prompts are not an authorization control. If a request arrives with a valid token, the API still decides whether the operation is permitted.
Log the agent identity, the delegating user where one exists, the tool, and the operation, so that every action can be attributed afterward.
Agent identity and authorization protocols are still active work. The IETF draft explicitly leaves them outside its scope, so there is no single settled agent-authentication standard to adopt. Use the scoped-token mechanisms your identity provider already supports and keep the scopes narrow.
4. Treat returned text as untrusted input
Operation descriptions and response bodies can influence the model’s next action, which makes them part of the attack surface. Build the following rules into the API and the tool layer:
- Keep control fields, such as status values, type enums and action identifiers, separate from free text written by users or third parties.
- Mark the provenance of free text so the agent can tell a customer’s message from an API instruction.
- Never place user-supplied text inside a trusted operation description.
- Enforce permission checks on the server for every write, regardless of what the agent has concluded from the data it read.
5. Set workload limits and recovery behavior
Choose limits that protect the API and the services behind it. Limits can be applied per user or account, per tool, and per agent or server, and the right scope depends on where capacity actually runs out. Request-count limits and token-volume limits can be separate constraints; OpenAI’s rate-limit documentation, for example, describes both as distinct and service-specific. Set your own figures from your capacity and policy.
When a limit is hit, return the feedback described in step 2 and let the client back off, using exponential backoff with jitter and a capped number of attempts. Define the behavior for each failure the agent is likely to meet:
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- Malformed or unexpected responses: stop the chain and return a structured failure instead of passing the raw body onward.
- Unavailable dependencies: return a partial result flagged as partial, queue the action, or escalate to a person.
- Rate limits: wait for the indicated period, then retry the same request, or fall back to a read-only path.
6. Monitor behavior, not just uptime
Track the metrics that show how agents use the API, because a healthy status page can hide a costly pattern:
- Which operations are chosen, and how often a chosen operation is followed by an error.
- Latency per operation and per tool.
- Response size and token size per call.
- Retry counts and the share of writes retried with the same key.
- Error rates by error code, and by caller identity.
Attribute cost to the agent, the tool, and the user or account, so that a single runaway workflow can be found and limited.
Design choices to settle before exposing the API
No single vendor or product comparison is established by the guidance reviewed here. The choices below are the ones that recur in agent-facing designs, and each one should be decided explicitly.
| Axis | Options | Decide by |
|---|---|---|
| Access model | User-delegated, per-user data; or machine-to-machine, scheduled or organisation-wide access | Whether the task should see one person’s data or the organisation’s |
| Operation risk | Read-only; reversible write; irreversible write | Whether the operation needs a preview, confirmation, or human approval |
| Limit scope | Per agent or server; per tool; per user or account; per downstream service | Which resource runs out first under load |
| API exposure | Full underlying API; or a smaller set of task-specific operations | How many operations the agent can reach without a deliberate choice |
| Recovery | Structured errors with retry guidance; fallbacks; preview or undo; escalation to a person | What the agent should do when a step fails midway through a chain |
What the standards and guidance say
IETF Internet-Draft on HTTP APIs consumed by AI agents
The most directly relevant document is Design Considerations and Profile for HTTP APIs Consumed by AI Agents, an Internet-Draft by M. Gaikwad published on 2026-06-30. It is an informational draft, not a standard, and it is listed to expire on 2027-01-01 unless it is renewed or advanced. It addresses the shape of HTTP APIs and their descriptions. It does not define a new identity or authentication protocol.
Its central idea is captured in this sentence:
“It treats the agent as a client whose behavior is shaped by the shape of the API.”
The draft also includes a security principle that matches the guidance above: “Enforce access decisions at the API.” It discusses APIs exposed through the Model Context Protocol (MCP), and it states that most of its concepts can be applied to GraphQL and gRPC. For GraphQL it flags query depth and query cost as particular concerns, because an agent can construct deeply nested queries that a fixed REST endpoint would not allow.
AWS Prescriptive Guidance on MCP governance
AWS Prescriptive Guidance’s “MCP governance strategy” covers authentication and authorization, load controls, and operational metrics for MCP server deployments. It is most useful for teams that expose tools through MCP servers and need a governance structure around them.
Australian Government agentic AI design statements
The Digital Transformation Agency’s Agentic AI Addendum statements: Design addresses government agencies. It says agencies must give agents appropriate authentication credentials and authorization. It also says agencies should limit the tools an agent can use, retain approvals for high-impact tools, and enable fallbacks for tool or API failures, timeouts, unexpected responses and rate limits. Its requirements apply to government agencies, so treat it as a worked governance example rather than a rule for private-sector APIs.
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OpenAI rate-limit documentation
OpenAI’s rate-limit documentation explains how request and token limits work and how responses signal them. Its figures are specific to that provider, change over time, and should not be used as benchmarks for another API. Its value here is in showing the mechanics of separate limit types.
What the evidence does not establish
The sources above do not publish failure rates for agent callers, productivity effects, or benchmarks comparing one API design with another. They also do not supply a universal set of limits. The recommendations in this article are engineering reasoning based on the guidance cited, and the thresholds, timeouts and page sizes you adopt should come from measurements of your own system.
When something goes wrong
Use the table below to match a symptom to its likely cause, then return to the step that addresses it.
Quick Recap
| Symptom | Likely cause | Where to fix it |
|---|---|---|
| The same payment or record is created twice | The retry used a new key, or the write endpoint does not honor keys | Step 2: tie the key to the action and return the stored result for repeated keys |
| The agent repeats the same failing call | The error does not say whether it is retryable | Step 2: add retry and action fields; step 5: cap retries in the client |
| The agent picks the wrong operation | Overlapping names or vague descriptions | Step 1: rename, state side effects, and remove operations the task does not need |
| Context fills quickly and token cost rises | No server-side page limit, or large fields returned by default | Step 2: enforce a maximum page size and return summaries with a fetch-by-ID operation |
| The agent acts outside the task’s scope | A broad or reused credential, or a missing server-side check | Step 3: issue task-scoped tokens and enforce scope on every request |
| The agent follows instructions found inside returned data | Free text is mixed with control fields | Step 4: separate fields, label provenance, and check permissions before any write |
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