Most AI agent demos look remarkable. Most AI agents in production look embarrassing. The gap between them is not the model — it is everything the demo conveniently skips: bad inputs, unexpected tool failures, edge cases that weren't in the training distribution, and users who will probe the limits the moment they sense one.
The first thing we do before writing a single prompt is define what the agent is allowed to do and, more importantly, what it is not allowed to do. A customer support agent that can issue refunds needs explicit rules about when it refuses — not because we don't trust the model, but because the model has no skin in the game and you do. Guardrails written in plain English before the first line of code force a clarity that saves weeks of iteration later.
Evals come next, before any UX work. We write 100–300 test cases that cover the happy path, the edge cases, and the cases where the agent should politely decline. We score them. That number is now the benchmark — every subsequent change either improves it or we revert. Without this baseline, you are shipping on vibes and you will feel the consequences at 2 AM.
Failure modes deserve more design time than happy paths. What happens when a tool call returns a 500? What happens when the user asks the agent to do something it was not trained for? What happens when the context window fills up mid-task? Agents that fail gracefully — with a clear explanation and a handoff path — preserve user trust. Agents that fail silently or confidently-wrong destroy it.
Observability is non-negotiable. Every tool call, every model response, every user input gets logged with a trace ID that ties the session together. When a user complains about a bad answer on a Tuesday morning, you want to replay the exact session in under sixty seconds. Without traces, every bug report is a guessing game.
The confidence problem is the hardest to solve. Language models are fluent. They produce wrong answers in the same tone as correct ones. The mitigation is not prompting the model to say 'I'm not sure' — that doesn't work reliably. The mitigation is retrieval grounding, confidence scoring, and scope limiting: the agent only answers questions in domains where you have measured its accuracy and found it acceptable.
The agents that survive production are boring in the right ways. They do a small number of things reliably. They refuse clearly when asked to do something outside their scope. They hand off to a human when confidence is low. They get better incrementally, one eval improvement at a time. Ship that version first. The ambitious version comes after you've earned trust.
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