Agents that demo well often collapse in production. Here's the engineering discipline that keeps them reliable, observable, and safe at scale.
The gap between an AI agent that impresses in a demo and one that survives production is enormous. In a demo, the happy path is all that matters. In production, the long tail is everything — malformed inputs, tool failures, ambiguous goals, and adversarial users.
The teams who succeed treat agents like any other distributed system: with observability, guardrails, and rigorous evaluation. Every tool call is traced. Every decision is logged. Every prompt change is measured against a benchmark before it ships.
Start with a tight scope. An agent that does one job exceptionally well beats a general agent that does ten things unreliably. Constrain the tool surface, define clear success criteria, and add human approval gates for high-stakes actions.
Finally, invest in evaluation early. A good eval harness turns 'it feels better' into 'accuracy improved 8 points.' That's the difference between shipping with confidence and shipping on vibes.
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