Building AI Agents That Actually Work in Production

Demos Are Cheap, Deployments Are Not

An agent that impresses in a recorded walkthrough usually fails in production for the same three reasons: it has no boundaries, no escape hatch, and no measurement. Everything that follows is about building those three in before the model is chosen, not after.

Guardrails That Actually Hold

Guardrails are an architecture problem. A prompt that says "never do X" is a request, not a control — the enforcement has to live outside the model.

• Give the agent a typed tool surface, not free-form shell or SQL access
• Cap every tool call with an explicit budget of money, rows, or API writes
• Validate arguments against a schema before execution, and log every rejection
• Route irreversible actions through a human approval step with a timeout
• Run a separate reviewer model on the plan, never on the drafted reply

Where Agent Projects Fail

Most failures we have audited were not model failures. They were product and operations decisions made early and never revisited.

• Automating a process nobody wrote down, so the agent inherits the ambiguity
• Measuring accuracy instead of the cost of a wrong answer
• Letting the agent retry silently until the task finally succeeds
• Shipping without a replayable trace of every decision it made
• Treating human escalation as a failure rather than a designed outcome