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

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.

1. Audit the existing AI integration against the five contracts.
2. Install the evaluation harness before any further model work.
3. Document fallbacks, latency budgets, and cost ceilings.
4. Wire telemetry, then iterate on quality.