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An API that AI agents can use reliably is an explicit, machine-readable contract—not merely a collection of endpoints. Publish an accurate schema, make every operation and field unambiguous, constrain inputs, define pagination and errors, enforce authorization in the application, and test complete tool-call traces. The model proposes a call; your integration layer must validate, authorize, execute, and report the result.
What reliability means for an agent-facing API
Traditional API clients are written by developers who already know the endpoint, parameter names, data types, and failure behavior. An AI agent has to infer those details from the tools and descriptions you expose, then select and populate a call from natural-language context. Reliability therefore has two dimensions:
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- Call formation: the agent chooses the right operation and supplies values in the accepted shape.
- End-to-end behavior: the application validates the call, applies permissions and business rules, performs the action safely, and returns a result the agent can interpret.
A schema can improve the first dimension without guaranteeing the second. A request may conform to JSON Schema and still be unauthorized, stale, contradictory to business rules, or dangerous to repeat. Treat the model-generated call as an untrusted request to an application service.
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Use OpenAPI for HTTP capabilities
OpenAPI is a language-agnostic description of HTTP APIs that lets people and computers discover capabilities without reading source code or inspecting network traffic. The official specification defines this purpose at spec.openapis.org/oas/latest.html. For an agent-facing service, the document should describe every operation, parameter, request body, response, error, authentication requirement, and meaningful constraint.
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Generate or validate the document in your build process, then publish the same version that the deployed service implements. A beautifully written schema that has drifted from production is worse than a smaller, accurate one: it teaches the agent behavior that the server will reject or interpret differently. Include valid examples and exercise those examples against a test or staging endpoint.
Give operations distinctive names and purposes
Names and descriptions are part of the tool-selection interface. Use a verb that identifies the action and a noun that identifies its target, such as search_invoices, get_invoice, or cancel_subscription. Avoid several operations whose names differ only by an internal implementation term.
Each description should state:
- What the operation does and what resource it affects.
- When an agent should use it instead of a nearby operation.
- Important limits, required preconditions, and side effects.
- Whether the operation only reads data or can create, change, or delete it.
- What the result contains and how an empty result is represented.
Keep descriptions concise enough to be read in a tool list, but do not omit a condition that changes the correct choice. These are practical design recommendations rather than a universal description template mandated by OpenAPI.
Version and review the contract with the service
Record the schema version alongside the deployed API version and make incompatible changes deliberate. A renamed field, changed enum meaning, new required property, or altered default can invalidate an existing agent integration even when the HTTP route remains the same. Run contract validation in continuous integration and reject examples that no longer match the schema.
Make tool inputs unambiguous and constrained
Use strict schemas where the tool runtime supports them
OpenAI’s function-calling guidance describes tools with JSON Schema and recommends strict mode when supported. In strict mode, calls are required to adhere to the declared schema instead of relying on best-effort matching. The guide documents constraints including additionalProperties: false and marking schema properties as required; represent an optional value with a nullable type rather than silently omitting a required property. Read the current compatibility notes for the model and API you use at developers.openai.com/api/docs/guides/function-calling.
Strict mode constrains shape, not truth. The server must still validate ranges, cross-field relationships, resource ownership, and current state.
Declare every property and reject undeclared fields
An object that accepts arbitrary properties gives an agent room to invent keys that your application ignores or misinterprets. For strict tool schemas, set additionalProperties to false at each object level where the runtime requires it. Enumerate allowed values with an enum, constrain strings with formats or length limits, and constrain numbers with minimum and maximum values when those limits are real.
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{
"type": "object",
"additionalProperties": false,
"properties": {
"subscription_id": {
"type": "string",
"description": "The subscription to cancel."
},
"effective_date": {
"type": ["string", "null"],
"format": "date",
"description": "Optional date; null means cancel at the earliest allowed date."
}
},
"required": ["subscription_id", "effective_date"]
}The nullable field is still required in this strict representation; the caller must send either a date or null. If your tool runtime has different strict-mode rules, adapt the schema and test an actual call rather than assuming portability.
Prefer constrained values to prose-only rules
Write a rule in the schema whenever a validator can enforce it. An enum such as pending, paid, and void is safer than telling the model to type one of those words. Use a pattern or format for identifiers, a bounded integer for page size, and an array item schema for batches. Keep business conditions that depend on live data in server-side validation.
Separate user intent from server-controlled data
Do not expose fields the caller should never choose, such as an account owner, audit timestamp, authorization decision, or computed total. The application should derive those values from authenticated context and current records. If a response includes a field that cannot be supplied or changed by a caller, mark that distinction clearly in the schema and description.
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Document field behavior, not only data types
Type information does not tell an agent whether omission is allowed, whether a value is writable, or what a default means. Google’s AIP-203 explains that behavior annotations communicate semantics to clients and that changing those semantics can be incompatible; see google.aip.dev/203.
Classify each field
- Required input: the caller must provide it for the operation to be meaningful.
- Optional input: omission has a documented meaning, such as using a server default.
- Nullable input: the caller may deliberately provide
null, which is different from omission. - Output-only: the service returns it; callers cannot set it.
- Immutable: it can be supplied at creation but cannot be changed later.
- Conditionally required: it is required only when another option has a particular value.
State defaults in ordinary language and in schema metadata. Explain units, time zones, inclusive or exclusive boundaries, case sensitivity, and whether an empty string differs from a missing value. For conditional rules, include a server error that identifies the missing dependency rather than relying on the model to infer it.
Make identifiers and dates operationally clear
Tell the agent whether an identifier is a database key, a human-visible number, or an opaque token. Specify the accepted date or timestamp format and the time zone used for comparisons. If a value is returned in a canonical form, say that callers should reuse the returned value rather than reconstructing it.
Design collection operations around continuation
Expose page size and a continuation token
Collection tools should define a page-size parameter, its default and maximum, the returned items field, and a continuation token. Google AIP-158 states that a page can contain fewer items—including zero—before the collection is exhausted; a short page is not proof that there are no more results. The guidance is at google.aip.dev/158.
| Element | Contract to document | Agent behavior |
|---|---|---|
page_size |
Default, maximum, and what happens when the caller exceeds the maximum | Request a reasonable size; do not assume the server honors an oversized value |
page_token |
Opaque continuation value, expiration, and whether it is tied to the original query | Return the token unchanged on the next request |
items |
Item schema and whether an empty array is valid | Process zero, one, or many items without treating zero as completion |
| Completion signal | Empty or absent next token, or another explicit rule | Continue until the documented completion condition |
Explain whether filters, ordering, and other query parameters must remain unchanged between pages. If a token captures a snapshot or ordering, say so; if results can change while the agent paginates, document that limitation. Never require the model to manufacture a token or decode one.
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Return stable, bounded results
Use deterministic ordering where possible and place a maximum on page size to protect latency and context limits. Include a concise summary of the collection state only when it is accurate and useful; do not make an agent infer totals from the number of items in one page.
Keep authorization and side effects in the application
A function call is a request for your application to use functionality or data. The integration layer—not the model—must authenticate the caller, authorize the target resource, enforce business rules, perform the action, and return an outcome. The function-calling documentation describes this application-mediated pattern at developers.openai.com/api/docs/guides/function-calling.
Use a server-side execution pipeline
- Parse: decode the tool arguments and reject malformed JSON or an unknown operation.
- Schema-validate: enforce required properties, types, enums, bounds, and disallowed properties.
- Normalize: canonicalize identifiers, dates, and casing without changing the user’s intended target.
- Authorize: check the authenticated principal, tenant, resource ownership, and required scope.
- Apply business rules: verify current state, dependencies, limits, and approval requirements.
- Execute: run the read or side effect with timeouts and transaction boundaries appropriate to the operation.
- Report: return a structured success or error object that states what happened and what the caller can do next.
Recheck permissions and resource state at execution time. A model may have selected an identifier from an earlier response that is now deleted, changed, or outside the caller’s authority.
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Mark mutating operations prominently and explain what they change, whether they can trigger notifications or charges, and which preconditions must hold. Decide explicitly whether repeating the same request is safe. A read is usually repeatable; a create, send, transfer, or delete may not be.
There is no single retry or idempotency policy that fits every API and transport. The correct contract depends on operation semantics. If a mutating operation supports an idempotency key, define its format, scope, retention, and response when the same key is reused. If it does not, tell clients not to retry automatically after an uncertain timeout. Google AIP-194 discusses automatic retry configuration, but it does not establish one cross-provider rule; consult google.aip.dev/194 alongside your own service semantics.
Return errors an agent can act on
Use a stable, structured error envelope. Although no single vocabulary is universal across API styles, the categories below distinguish the decisions an agent and its application must make.
| Category | Typical cause | Useful response fields |
|---|---|---|
| Invalid input | Wrong type, missing conditional field, out-of-range value, or unknown property | Machine-readable code, field path, accepted constraint, and whether correction is possible |
| Unauthenticated or forbidden | Missing credentials or insufficient permission | Code, required scope or action, and a safe remediation message; do not disclose protected data |
| Not found | Identifier does not exist or is not visible to this caller | Stable code and the identifier type; avoid revealing whether a hidden resource exists |
| Conflict | State changed, version mismatch, duplicate, or violated uniqueness rule | Current-state details, conflicting field, and whether the caller should reread or choose another value |
| Rate limited | Quota or request-rate limit exceeded | Retry-after information when available and the affected limit |
| Transient failure | Timeout, dependency outage, or temporary overload | Whether the outcome is known, retry guidance for this operation, and a correlation ID |
Keep error messages safe to send back to the model: include enough context to correct a call, but exclude secrets, tokens, stack traces, and unrelated customer data. Return a consistent content type and envelope for both validation and execution failures so the integration layer does not need operation-specific parsing.
Separate predictable results from prose
Success responses should have a stable shape and explicit empty-state behavior. An agent should be able to distinguish “no matching records,” “the operation completed with no body,” and “the service failed.” Use named fields rather than positional arrays, include resource identifiers and status values, and return server-generated values needed for a follow-up call.
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For a mutating operation, state whether the response represents an accepted request, an operation still in progress, or a completed change. If work is asynchronous, provide an operation identifier and a status-check operation with documented terminal states. Do not make the agent scrape human-formatted log text to discover success.
Build a tool surface agents can navigate
Expose focused operations
One broad endpoint with a command string and dozens of optional fields forces the model to infer an internal mini-language. Prefer focused operations with a small, coherent argument set. Split unrelated side effects, such as previewing a change and committing it, into separate tools when the distinction matters for safety.
Use resource names consistently
Choose one naming convention for identifiers, parent resources, timestamps, pagination, and status fields. If one operation calls a value customer_id and another calls the same concept account, the model must guess whether they are interchangeable. Consistency also makes generated clients and contract tests easier to maintain.
Make destructive actions deliberate
Require the exact resource identifier and any genuinely necessary confirmation or precondition for irreversible actions. A confirmation field should express a meaningful condition, not merely add a word the model can repeat automatically. Where possible, offer a read-only preview that returns the expected impact before the mutating call.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Validate the complete path, not only the schema
Schema validation answers whether arguments have the right shape. Reliability testing must also ask whether the right tool was selected, whether authorization and business rules were applied, and whether the returned result lets the agent recover.
Use a representative trace set
Construct evaluation cases from real user intents and include both successful and adversarial variations. For each trace, record the proposed operation, arguments, validation outcome, authorization decision, executed effect, latency or timeout, and final response. OpenAI’s function-calling guidance includes evaluation considerations for this application-mediated workflow; use it as a starting point at developers.openai.com/api/docs/guides/function-calling.
| Scenario | What to verify |
|---|---|
| Valid single-resource request | Correct operation, exact identifier, successful result, and no unintended side effect |
| Missing or ambiguous argument | The agent asks for or obtains the needed value; the server returns a precise validation error if it proceeds incorrectly |
| Unknown property or wrong type | Strict validation rejects the call without silently dropping data |
| Permission failure | No protected information leaks and the response identifies the authorization problem safely |
| Not-found or stale identifier | The result is distinguishable from a transient outage and supports correction |
| Empty collection | The agent reports no matches rather than claiming the service is unavailable |
| Multi-page collection | The agent preserves filters, follows continuation tokens, and stops only at the documented completion signal |
| Timeout after a side effect | The service communicates whether the outcome is known and applies the documented retry or idempotency behavior |
| Repeated request | Duplicate effects are prevented or clearly exposed according to the operation’s contract |
Monitor production outcomes
Log a correlation ID, schema version, operation name, validation result, authorization result, latency, and safe error code. Redact credentials and sensitive payloads. Track where calls fail: wrong tool selection, malformed arguments, denied access, business conflicts, dependency errors, or an inability to interpret the result. A rising validation-error rate may indicate a description or schema problem; a rising authorization rate may indicate missing context or an overly broad tool surface.
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When choosing between alternative designs, score both the contract and the execution path on the following axes:
- Discoverability: Can an agent identify the right operation from names, descriptions, and an accurate OpenAPI document?
- Semantic clarity: Are required, optional, output-only, immutable, and conditional fields explicit?
- Input constraints: Do strict schemas, enums, bounds, and server validation prevent invented or unsafe values?
- Collection traversal: Are page sizes, continuation tokens, ordering, and completion conditions defined?
- Control boundaries: Does the application, rather than the model, enforce authentication, authorization, and business rules?
- Interpretability: Can the agent distinguish success, empty results, conflicts, permission failures, and uncertain outcomes?
- Evaluability: Can you replay representative traces and monitor failures without exposing sensitive data?
This framework compares practical reliability characteristics; it is not a published benchmark or a numerical reliability guarantee.
A practical implementation sequence
- Inventory capabilities: list the read and write actions an agent genuinely needs, along with resources, side effects, and authorization scopes.
- Write the contract first: create or update the OpenAPI paths, schemas, examples, response envelopes, and error definitions.
- Annotate behavior: mark required, optional, nullable, output-only, immutable, and conditionally required fields; document defaults and units.
- Constrain tools: enable strict mode where supported, reject undeclared properties, and use enums, formats, and bounds.
- Design pagination: add bounded page size and opaque continuation tokens; state the completion signal and query-stability rule.
- Implement the execution pipeline: parse, validate, normalize, authorize, enforce business rules, execute, and return a structured result.
- Define repeat behavior: document idempotency keys, safe retries, and uncertain outcomes separately for each side-effecting operation.
- Run trace evaluations: cover valid, invalid, ambiguous, unauthorized, empty, multi-page, timeout, and repeated-call cases.
- Deploy with observability: version the schema, monitor safe error categories and latency, and investigate drift between the document and production.
Common failure modes and fixes
The agent invents parameters
Cause: the schema permits arbitrary properties or the description implies capabilities that are not declared. Fix: set additionalProperties to false where supported, enumerate fields, and reject unknown keys at the server boundary.
The agent omits a value that the server needs
Cause: a required field is described only in prose, or an optional field has an undocumented conditional requirement. Fix: mark the property required when appropriate, represent nullable values explicitly, and return a field-specific error that states the condition.
The agent stops after a short page
Cause: the tool does not define continuation behavior or the agent assumes item count equals completion. Fix: return an opaque token, document the end condition, and state explicitly that a page can contain fewer items—including zero—before the end.
A retry creates a duplicate effect
Cause: a timeout leaves the outcome uncertain and the operation has no documented idempotency contract. Fix: define an idempotency key or prohibit automatic retries for that operation; return enough status information to reconcile an uncertain result.
The call is valid but still unauthorized or unsafe
Cause: schema validation is being treated as permission or business validation. Fix: perform authorization and current-state checks after parsing and before execution, and never trust model-supplied ownership or audit fields.
The schema and deployed behavior diverge
Cause: documentation is updated separately from implementation or examples are never executed. Fix: validate the published document in continuous integration, run examples against a representative environment, version changes, and monitor contract errors after deployment.
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Release checklist
- Every exposed operation has a distinctive name, purpose, side-effect statement, and valid example.
- The OpenAPI document matches deployed routes, request schemas, responses, authentication, and errors.
- Strict tool schemas are enabled where compatible; undeclared properties are rejected.
- Required, optional, nullable, output-only, immutable, and conditional fields are explicit.
- Defaults, units, formats, time zones, limits, and enum meanings are documented.
- Collection tools define page size, continuation token, ordering or filter stability, and completion behavior.
- The application validates, authorizes, enforces business rules, and executes independently of the model.
- Each mutating operation documents side effects, repeat behavior, idempotency, and retry handling.
- Errors distinguish invalid input, permission, missing resource, conflict, rate limit, and transient failure.
- Success responses distinguish completed, accepted, empty, and in-progress outcomes.
- Trace tests cover ambiguous input, failures, pagination, timeouts, and repeated requests.
- Production logs and metrics expose contract and outcome failures without recording secrets.
The most dependable agent API is not the one with the cleverest prompt. It is the one whose capabilities, constraints, state transitions, and failures are explicit enough for a model to choose a call—and whose application layer is rigorous enough to treat that call as an untrusted request.
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