Start with explicit boundaries
The biggest mistake in AI-native software is letting model calls sprawl across the codebase. We enforce clear boundaries: ingestion, reasoning, action, and observability are separate modules with typed interfaces.
This makes it possible to swap models, add guardrails, and debug failures without untangling spaghetti.
Use an event-driven backbone
Long-running agent tasks belong in a job queue or workflow engine — not in HTTP request handlers. We use event-driven architectures so automations survive restarts, retry safely, and scale horizontally.
Your API layer should trigger work and return status. The heavy lifting happens asynchronously with idempotent steps.
Design human-in-the-loop from day one
Every system that touches revenue, compliance, or customer trust needs escalation paths. We build approval queues, confidence thresholds, and audit logs as first-class features — not afterthoughts.
Operators should supervise outcomes, not babysit individual API calls.
Observability is non-negotiable
Log every model input and output in a structured, searchable format. Track latency, cost per run, and failure modes. Run offline evals before deploying prompt changes.
Production AI without observability is flying blind. Your future self (and your client) will thank you.