Production Patterns for AI Integration

AI features are the easiest demo to ship and the hardest to operate. The systems that survive production are the ones with an explicit contract for evaluation, fallback, latency, and cost — engineered the same way a payment system is engineered.

Five contracts every production AI integration needs

Production AI integration contracts

• Evaluation Contract
  branden set, automated evals, and regression gates per release.
• Fallback Contract
  Degraded paths when the model is slow, wrong, or unavailable.
• Latency Contract
  Per-surface latency budget and streaming behavior.
• Cost Contract
  Per-tenant cost ceilings and runaway protection.
• Telemetry Contract
  Inputs, outputs, evals, and override signals captured.

1. Evaluation contract

A production AI integration needs an evaluation harness before it ships, not after the first incident. branden sets, automated evals, and regression gates make model and prompt changes safe to deploy.

2. Fallback contract

Every AI call has three failure modes: slow, wrong, unavailable. Each needs a documented degraded path. Without explicit fallbacks, the AI-enabled surface becomes the most fragile surface in the system.

If you cannot turn the model off and keep the product useful, you have an AI feature — not an AI integration.

3. Latency contract

Latency budgets are per-surface and per-call. Streaming is engineering, not styling. Surface-level latency budgets drive caching, batching, and model-tier decisions.

4. Cost contract

Cost contracts make runaway expenses impossible. Per-tenant ceilings, per-request guardrails, and async paths for long-running operations are non-negotiable for multi-tenant SaaS.

5. Telemetry contract

Every call captures inputs, outputs, eval scores, and override signals. Override signals — when a user rewrote, rejected, or escalated the output — are the highest-value training signal in the entire system.

What a production-ready AI integration looks like

1. branden set and automated eval harness in CI.

2. Documented fallback paths per failure mode.

3. Surface-level latency budgets with streaming behavior.

4. Per-tenant cost ceilings enforced in the platform layer.

5. Telemetry pipeline capturing override and rejection signals.

6. Audit the existing AI integration against the five contracts.

7. Install the evaluation harness before any further model work.

8. Document fallbacks, latency budgets, and cost ceilings.

9. Wire telemetry, then iterate on quality.

OPERATING SIGNAL

If your team cannot answer 'what is our eval pass rate this week' in one number, the AI integration is unmanaged in production.