Building production AI agents that don't fall over in week three
Most AI demos work. Most AI products don't. Here's the engineering discipline behind agents that survive contact with real users — evals, guardrails, and the boring infrastructure that makes the magic durable.
Why most AI projects fail at week three
The first demo always works. The third week is where AI projects die. Here's the pattern we keep seeing: a brilliant prototype lands, the team ships it to a small group of users, edge cases multiply, and within a sprint the engineers are firefighting hallucinations and retries instead of building.
The fix is not a better model. It's better software engineering around the model.
The boring stack that makes AI durable
Three layers separate demos from products: deterministic orchestration, eval-first delivery, and observability tuned for non-determinism. Skip any of them and you'll be on call for your prompts.
Deterministic orchestration. State machines or workflow engines (Temporal, LangGraph) wrap the LLM so retries, branching, and failure modes are explicit. The model gets to be creative; the system gets to be reliable.
Eval-first delivery. Before a prompt change goes to production, it runs against a frozen dataset and a regression suite. We measure pass rate, latency, and cost — and we don't merge changes that move any of those the wrong way.
Observability for non-determinism. Token-level tracing, per-step latency, per-decision cost. You see what the model did, what it cost, and why. Without this, debugging is guesswork.
The cultural fix
AI teams that ship treat the LLM as a component, not the product. The product is the experience around it — and that experience is built with the same rigor as any other production system.
If your AI roadmap reads like a list of demos, you're going to have a hard third week. Build the boring stack first.