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Make the API an explicit, machine-readable contract rather than a collection of loosely documented endpoints. Give each operation a precise purpose, constrain every argument with a schema, label field behavior, define pagination and errors, and keep validation, authorization, business rules, and side effects in your application. Then test complete tool-call traces—including malformed requests and failures—against that contract.
What reliability means for an agent-facing API
An AI agent does not experience an API the way a human developer does. It selects an operation from descriptions, constructs arguments from a schema, sends the request through an application integration layer, interprets the response, and decides what to do next. Reliability therefore has two parts:
- Call reliability: the agent chooses the right operation and produces valid arguments.
- Outcome reliability: the service validates the request, applies permissions and business rules, performs the intended action once or according to its documented semantics, and returns an interpretable result or error.
A schema can improve the first part without guaranteeing the second. Treat the interface as a contract shared by the model, the integration code, and the server. OpenAPI describes this role well: “The OpenAPI Specification (OAS) defines a standard, programming language-agnostic interface description for HTTP APIs, which allows both humans and computers to discover and understand the capabilities of a service without requiring access to source code, additional documentation, or inspection of network traffic.” See the OpenAPI Specification (version 3.2.1 published at the time of access).
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Reliability is not a single percentage. No universal reliability figure or retry policy applies to every API. Your operation semantics, transport, data model, model, and integration code determine the appropriate contract.
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Make capabilities discoverable
Publish an accurate OpenAPI description
For HTTP services, publish an OpenAPI document that describes paths, methods, parameters, request bodies, response bodies, authentication requirements, status codes, and examples. Keep the document synchronized with deployed behavior. An elegant schema that is stale is worse than a smaller, accurate one because it teaches the agent a capability that does not exist or omits a constraint the server enforces.
- Validate the document in CI and reject invalid changes before deployment.
- Generate or check examples from the same schemas used by the server where practical.
- Document authentication and authorization requirements without placing credentials in examples.
- Describe limits such as maximum page size, upload size, date ranges, and rate limits next to the affected operation.
- Version breaking changes and keep old contracts available for clients that still use them.
Give every operation a distinctive purpose
Operation names and descriptions are part of the model’s decision surface. Use a short, distinctive name and explain what the operation does, when to use it, the resource or scope it affects, and important side effects. “Update customer” is less useful than “update_customer_billing_email,” which states the narrow mutation and reduces accidental use for unrelated profile data.
| Operation description element | What to state | Example |
|---|---|---|
| Purpose | The single job the operation performs | “Returns invoices for one account.” |
| When to use | The user intent or state that calls for it | “Use when the user asks to review billing history.” |
| Scope | Resource, tenant, account, or time boundary | “Only invoices owned by the authenticated account are returned.” |
| Side effects | Whether data changes, a message is sent, or a charge can occur | “Creates a draft; does not submit it for payment.” |
| Limits | Pagination, size, rate, or conditional restrictions | “At most 100 records per page; use the continuation token for more.” |
Descriptions should be concise enough for a model to use, but they must not hide important limits in prose elsewhere. Distinct names also help evaluation: you can tell whether the agent selected the intended capability rather than a similarly named one.
Constrain tool inputs with explicit schemas
Use JSON Schema and strict mode where supported
Tool definitions should express the argument shape in JSON Schema. OpenAI’s function-calling guide recommends strict mode where the API and model support it. Its documented strict-mode constraints include setting additionalProperties to false and marking schema properties as required; represent values that may be absent with nullable types rather than leaving required properties ambiguous.
A minimal tool definition might look like this:
{
"type": "function",
"name": "list_invoices",
"description": "Return invoices for the authenticated account. Use for billing-history questions; this operation does not create or pay invoices.",
"strict": true,
"parameters": {
"type": "object",
"additionalProperties": false,
"properties": {
"account_id": {
"type": "string",
"description": "Account whose invoices to list; must belong to the authenticated principal."
},
"page_size": {
"type": ["integer", "null"],
"description": "Maximum number of items to return; null uses the service default."
},
"page_token": {
"type": ["string", "null"],
"description": "Continuation token returned by the previous page, or null for the first page."
}
},
"required": ["account_id", "page_size", "page_token"]
}
}The exact syntax varies by tool API, so verify compatibility with the model and endpoint you use. Strict validation is a first line of defense, not a replacement for server-side validation: a syntactically valid string can still identify a different tenant, violate a business rule, or be unsafe to execute.
Make invalid states difficult to express
- Use an enum for a closed set such as
"status": "PAID" | "OPEN" | "VOID", rather than asking the model to remember allowed spellings. - Set numeric minimums and maximums for quantities, page sizes, and timeouts.
- Use explicit formats for dates, date-times, identifiers, and email addresses, then validate the values on the server as well.
- Use one field for one concept. Do not overload a string that sometimes means an ID and sometimes means a display name.
- Disallow undeclared object properties when strict mode requires it. Unexpected keys should be rejected or safely ignored according to a documented policy; silently interpreting them as commands is unsafe.
Descriptions should explain units, time zones, inclusivity of ranges, and whether a value is a resource identifier or human-readable text. If a filter accepts a date range, state whether the end date is inclusive and which time zone is applied.
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Document field behavior, not just data types
Types alone do not tell an agent what it may change. Google’s AIP-203 describes behavior annotations that communicate semantics to clients and warns that changing those semantics can be incompatible.
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| Behavior | Meaning for callers | Design guidance |
|---|---|---|
| Required input | Must be supplied for the operation to run | Mark it required in the schema and return a targeted validation error when absent. |
| Optional input | May be omitted or set to a documented default | State the default and whether omission differs from an explicit null. |
| Output-only | Returned by the service but not accepted as an instruction | Do not ask the agent to provide it; ignore or reject it in request bodies. |
| Immutable | Set at creation and never changed | Reject update attempts instead of silently ignoring them. |
| Conditional | Required or allowed only when another field has a particular value | Describe the condition in both schema metadata and ordinary language. |
For example, an invoice’s invoice_id, created_at, and payment_status might be output-only, while currency could be immutable after creation. If an update accepts only billing_email, expose that narrow operation or a request schema that clearly excludes immutable fields. Avoid a broad “patch anything” tool unless its field-level rules are machine-readable and enforced.
Explain defaults and nullability
“Optional” can mean omitted, null, or an empty value, and those states often have different effects. Choose a policy and document it. A nullable page token can use null for the first page; an omitted notification preference might mean “leave unchanged,” while null might mean “clear it.” Make the distinction explicit in the schema and examples.
Design collection operations for complete traversal
Agents frequently need all matching records, not just the first response. Give every list operation a page-size parameter and a continuation token, and document the defaults, maximum, token lifetime, and whether filters and ordering must remain unchanged between requests.
Google AIP-158 specifically cautions that a short page—including a page with zero items—may occur before the end of a collection. An agent must not infer completion from items.length < page_size or from an empty page alone. Follow the token until the API indicates that no token remains.
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{
"items": [
{ "invoice_id": "inv_123", "status": "OPEN" }
],
"next_page_token": "eyJvZmZzZXQiOjEwMH0",
"total_size": null
}Use a stable ordering, such as creation time plus a unique ID, so records do not move unpredictably between pages. State whether the continuation token captures the original filter and ordering. If the token becomes invalid, return a structured error telling the caller whether to restart from the first page.
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Tell the agent when to stop
- Continue while
next_page_tokenis non-empty. - Send the same filter and ordering parameters unless the contract explicitly permits changes.
- Honor the server’s page-size limit even if the requested value is larger.
- Protect against loops by detecting a repeated token and surfacing an integration error rather than fetching forever.
Keep validation, authorization, and side effects in the application
A model-generated tool call is a request to your application, not a direct grant of authority. The integration layer should parse the call, validate it against the current schema, identify the authenticated user and tenant, check authorization, enforce business rules, call the service, and return a bounded result. OpenAI’s function-calling documentation describes this pattern: function calling connects models to functionality and data provided by the application, and its evaluation guidance covers testing the complete interaction.
- Parse and validate the envelope. Reject malformed JSON, unknown operations, undeclared properties, invalid formats, and values outside documented ranges.
- Resolve identity and scope. Derive the principal, tenant, and allowed resources from trusted authentication context, not from a model-supplied account ID alone.
- Authorize the requested action. Check permissions for the specific resource and operation. A valid schema does not make an action permitted.
- Apply business rules. Check state transitions, ownership, quotas, approval requirements, and conflicts using authoritative data.
- Execute with bounded effects. Set timeouts, limit result size, and isolate operations that can send messages, change records, or move money.
- Return an interpretable result. Include the fields needed for the next decision and omit secrets, internal stack traces, and unrelated records.
For high-impact actions, consider a two-step design: a preview or validation operation followed by an explicit commit operation. The preview can show the exact change, affected resources, and warnings; the commit still rechecks authorization and current state.
Return errors an agent can act on
Do not make the agent infer failure from a generic 500 response or a human-only sentence. Use stable error categories, machine-readable fields, and a clear indication of whether retrying can help.
| Failure class | Useful response information | Likely agent behavior |
|---|---|---|
| Invalid input | Field path, rejected value category, and correction guidance | Fix arguments and try once, or ask the user for the missing value. |
| Unauthenticated | Authentication requirement and recovery route | Stop and request sign-in or a renewed credential. |
| Permission denied | Resource and required permission, without leaking protected details | Do not retry unchanged; explain the access limitation. |
| Not found | Resource type and identifier context | Verify the identifier or tell the user it is unavailable. |
| Conflict | Current state, conflicting version, or required next step | Refresh state, obtain confirmation, or choose a different transition. |
| Rate limited | Retry-after information when available and request scope | Back off according to the documented transport policy. |
| Transient service failure | Stable code, request ID, and whether the operation’s effect is known | Retry only when the operation contract permits it; otherwise surface uncertainty. |
Keep error codes stable even if prose changes. Include a request or trace ID so operators can correlate the agent turn with server logs. Never return credentials, hidden prompts, raw SQL, or stack traces merely to make debugging easier.
Specify idempotency and retry behavior per operation
Repeating a read is usually different from repeating a side-effecting write. A retry after a timeout may arrive after the server has committed the change, so the client needs to know whether it can safely repeat the request or must first query status.
- For create or charge-like operations, support an idempotency key when appropriate and state its scope, retention period, and behavior when the same key is reused with different parameters.
- For updates, document whether the operation is a full replacement, a patch, or a state transition, and whether repeating it is harmless.
- For deletes, specify whether a second request returns success, not found, or a distinct already-deleted result.
- For reads, document transport-level retry guidance and any consistency delay that affects an immediate follow-up read.
There is no single cross-provider retry rule. Google’s AIP-194 addresses automatic retry configuration, but the appropriate policy still depends on the operation and transport. State the policy in the operation’s documentation and error model rather than assuming every failure is retryable.
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Evaluate real agent traces, not schemas alone
Schema validation proves that a call has the right shape. It does not prove that the agent selected the right operation, understood the field semantics, handled pagination, or produced the correct end-to-end result. Build an evaluation set around realistic conversations and record both tool-call formation and outcome correctness.
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- Valid requests with typical values and boundary values.
- Missing required arguments, null values, wrong formats, unknown properties, and out-of-range numbers.
- Ambiguous user requests that should trigger a clarifying question rather than a mutation.
- Empty collections, one-page collections, and multi-page collections with a short or zero-item intermediate page.
- Expired or invalid continuation tokens.
- Permission failures, cross-tenant identifiers, missing resources, conflicts, rate limits, and timeouts.
- Repeated requests after a network interruption, especially for side-effecting operations.
Measure the whole path
For each trace, capture the selected operation, arguments after validation, authorization result, downstream response, retries, final user-visible answer, latency, and cost. A “correct tool call” that produces an incorrect or unauthorized result is still a failed interaction. Redact personal and secret data before storing traces, and retain enough identifiers to correlate a model turn with server logs.
Use failures to improve the contract: rename ambiguous operations, add a missing enum or constraint, clarify a default, split a broad mutation, or change the response so the next decision is unambiguous. Do not claim reliability from a single successful demonstration.
How to compare two API designs
When choosing between alternative interfaces, score each design against the same practical axes:
| Axis | Questions to ask |
|---|---|
| Discoverability | Can a model and a developer identify the correct operation without reading source code? |
| Schema completeness | Are types, enums, formats, limits, defaults, and examples accurate? |
| Field semantics | Are required, optional, output-only, immutable, and conditional fields explicit? |
| Input enforcement | Does strict tooling reject undeclared properties, and does the server independently validate values? |
| Collection traversal | Are page size, continuation tokens, stable ordering, and completion rules clear? |
| Authority boundaries | Does the application—not the model—decide identity, authorization, and business-rule outcomes? |
| Error and result interpretation | Can the agent distinguish a fixable argument error from a permission, conflict, or uncertain commit? |
| Evaluation and monitoring | Can you replay representative traces and detect regressions in both calls and outcomes? |
A narrower operation often wins over a flexible one when the cost of an accidental side effect is high. A broader operation may be justified when its schema and authorization model are equally explicit and the server can safely reject unsupported combinations.
A practical implementation workflow
- Inventory capabilities. List the user goals your agent must support, the resources involved, read versus write operations, and actions that need confirmation.
- Define operation boundaries. Give each capability one purpose. Split unrelated mutations and avoid names that differ only by a vague verb such as “manage.”
- Write the contract first. Add request and response schemas, field behavior, status codes, errors, examples, limits, and authentication requirements to OpenAPI or your tool-definition format.
- Constrain inputs. Add enums, ranges, formats, nullability, required properties, and
additionalProperties: falsewhere strict mode requires it. Check the selected model and tool API for compatibility. - Implement server validation. Treat every model argument as untrusted input. Validate again after parsing and before any database or external-service call.
- Enforce authority. Resolve the authenticated principal, check tenant and resource access, apply business rules, and make high-impact writes explicit.
- Implement collection semantics. Add page size, continuation tokens, stable ordering, token validation, and a documented completion signal.
- Define failure behavior. Create stable error categories, correction details, request IDs, timeout behavior, and operation-specific retry or idempotency rules.
- Generate adversarial traces. Test ambiguity, malformed arguments, permission boundaries, empty and multi-page results, conflicts, timeouts, and repeated writes.
- Monitor and revise. Compare traces to the contract after every schema or model change, and update descriptions when deployed behavior changes.
Troubleshooting common agent failures
| Symptom | Probable contract problem | Remedy |
|---|---|---|
| The agent chooses the wrong endpoint | Overlapping names or descriptions | Make operation purposes and exclusions distinctive; split broad tools. |
| Arguments contain invented keys | Loose schema or non-strict tool configuration | Use strict mode where supported, set additionalProperties to false, and reject unknown fields server-side. |
| The agent sends an output field back on update | Input/output behavior is undocumented | Mark output-only and immutable fields and expose a narrower update schema. |
| Results stop after the first page | Completion inferred from item count | Document continuation-token traversal and test short and empty intermediate pages. |
| A denied request is retried repeatedly | Permission errors resemble transient failures | Return a distinct permission code and tell the caller that unchanged retries will not help. |
| A timeout creates a duplicate record | No idempotency or uncertain-commit contract | Add an idempotency key or a status-check operation and document safe retry behavior. |
| The call is valid but the result is wrong | Only schema conformance was tested | Evaluate authorization, business rules, downstream effects, and final user-visible outcomes. |
Reference design: a safe invoice workflow
Consider an agent that helps a user inspect and prepare an invoice. Expose separate capabilities rather than one unrestricted billing tool:
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This separation gives the model clear choices, lets the application put stronger controls around the commit, and makes traces easier to evaluate. The server remains responsible for deciding whether the invoice can be changed; the model only proposes a structured request.
Final design checklist
- Is every operation discoverable from an accurate, current description?
- Does each operation state purpose, scope, limits, and side effects?
- Are arguments constrained by a compatible JSON Schema and strict mode where supported?
- Are required, optional, nullable, output-only, immutable, and conditional fields explicit?
- Can an agent traverse every collection using documented continuation semantics?
- Does the application validate, authorize, and enforce business rules independently?
- Do errors distinguish correction, permission, absence, conflict, throttling, and transient uncertainty?
- Is retry or idempotency behavior defined for each side-effecting operation?
- Have you tested ambiguous, invalid, paginated, unauthorized, timed-out, and repeated calls?
- Can operators correlate traces, downstream effects, and user-visible outcomes without exposing secrets?
The dependable pattern is straightforward: publish a precise contract, make invalid calls difficult to form, keep authority in the application, make traversal and failure behavior explicit, and validate the complete agent interaction in production-like tests. Reliability emerges from all five layers working together, not from a schema alone.
Frequently Asked Questions
Should every API endpoint become an AI tool?
No. Expose capabilities the agent genuinely needs, with narrow purposes and clear authority boundaries. Keep internal or high-risk endpoints out of the tool surface unless their validation, authorization, and side effects are explicitly controlled.
Can strict tool schemas replace server-side validation?
No. Strict schemas constrain the generated call, but the server must still validate values, identity, permissions, current resource state, and business rules before executing anything.
How should an agent know that a list response is complete?
Use the documented continuation-token contract. Continue while a next-page token is returned; do not infer completion from a short or empty page.
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