AI API Design Checklist

Exposing AI capability through an API, whether to internal teams, partners, or products, adds concerns that ordinary API design does not address: outputs are probabilistic, latency is variable and often long, every call costs money, and safety controls must hold regardless of caller. This checklist covers what to design in. It complements how to build an ai api, ai api development, and api-first development, and reflects the standards in FISTA Solutions' AI enablement platform work.

Who should use this checklist?

Engineers designing endpoints that expose LLM, retrieval, or agent capabilities, platform teams standardizing AI APIs, and architects reviewing integration designs.

Are contracts typed and expressive?

1. Request schemas with types, constraints, and size limits; validation at the boundary.
2. Response schemas with structured fields, not only free text.
3. Confidence or uncertainty fields where meaningful.
4. Citations and provenance for grounded outputs.
5. Explicit refusal and escalation representations rather than empty results.
6. Metadata for model and prompt version used.

Reference: what is structured output.

Is streaming designed in?

Element	Present?
Streaming mode for long outputs with typed events
Partial results and final status semantics
Cancellation by the caller
Error events mid-stream
Non-streaming alternative for batch callers
Timeouts and keep-alive behavior documented

Reference: real-time web applications.

Are long-running operations handled?

1. Asynchronous pattern for work exceeding request timeouts: job creation, status, result retrieval.
2. Webhooks or polling with backoff guidance.
3. Progress reporting where useful.
4. Retention of results defined.

Are operations idempotent where they have effects?

1. Idempotency keys on write operations.
2. Deduplication window documented.
3. Safe retries for callers and agents.

Reference: how to build tool use for llm agents.

Are errors structured?

1. Stable error codes with messages.
2. Retryable versus terminal classification.
3. Policy and validation failures identified with the rule that fired.
4. Rate and spend limit responses with retry guidance.
5. Consistent semantics across endpoints.

Is behavior versioned?

1. Interface versioning for schema changes.
2. Model and configuration versions discoverable and pinnable by callers.
3. Change notices before defaults change; re-evaluation before promotion.
4. Deprecation policy with timelines.

Reference: how to build a prompt management system and how to build an llm gateway.

Are callers authenticated and scoped?

1. Authentication through the identity provider or API keys with rotation.
2. Scopes per capability and per data domain.
3. Delegated user context where the API acts on behalf of users, so downstream permissions apply.
4. Tenant isolation enforced.

Reference: api security best practices and ai access control.

Are limits enforced?

1. Rate limits per caller and tenant.
2. Spend budgets with alerts and hard limits.
3. Input and output size caps.
4. Concurrency limits for expensive operations.
5. Degradation behavior on limit rather than silent failure.

Reference: the ai cost optimization checklist.

Are safety controls independent of caller?

1. Output validation and policy checks applied server-side.
2. Injection defenses for content passed by callers.
3. Prohibited operations unavailable regardless of scope.
4. Consequence gates for actions exposed through the API.

Reference: the LLM security checklist.

Is the API observable?

1. Traces per request with model and prompt versions, latency per step, tokens, and cost.
2. Attribution to caller, feature, and tenant.
3. Quality sampling hooks.
4. Usage reporting exposed to callers.

Reference: the ai observability checklist.

Is the API documented and testable?

1. Reference documentation generated from contracts.
2. Examples including streaming, errors, and idempotent retries.
3. Sandbox or test mode without cost or side effects.
4. Contract tests in CI.
5. Behavioral evaluation results published for the default model version.

How should gaps be handled?

Authentication, scoping, safety, and limit gaps block exposure to any caller. Contract, error, and versioning gaps block exposure beyond a single internal consumer. Documentation and observability gaps are closed before general availability.

What does a well-designed endpoint look like in practice?

A grounded question-answering endpoint accepts a typed question with tenant and user context, streams typed events with partial text and a final message carrying citations, confidence, and the model and prompt versions used, returns a structured refusal when evidence is missing, applies per-tenant rate and spend limits, and emits a trace with token counts and cost attributed to the calling feature.

How FISTA Solutions designs AI APIs

FISTA Solutions designs AI APIs to this checklist within its AI enablement platform practice: typed contracts with confidence and citations, streaming and asynchronous patterns, idempotent writes, structured errors, behavior versioning tied to the prompt management system and gateway, authenticated and scoped callers, enforced limits, caller-independent safety, and full observability with cost attribution. AI agents and product features built by the web and mobile practice consume them, and forward deployed engineers integrate them with client platforms. The record behind the approach is 150+ projects with 99.9% uptime.

To review an AI API design, message FISTA on WhatsApp, or read rest vs graphql for ai apis for the interface style decision.