What actually breaks when you put AI agents into production

Every team can build an impressive agent demo in an afternoon. The gap between that demo and a production system your operations team trusts is measured in months, and almost none of that time is spent on prompting.

The first thing that breaks is evaluation. Without a golden dataset and a scored regression suite, every prompt change is a coin flip. We treat prompts like code: versioned, tested against fixtures and promoted through environments.

The second is cost. Token spend scales with usage in ways engineers consistently underestimate. Routing cheap deterministic work to smaller models, caching retrieval results and truncating context aggressively typically cuts inference cost by half without any measurable quality loss.

The third is trust. Agents need bounded permissions, human approval steps for irreversible actions and a complete audit trail. When an agent sends a customer the wrong invoice, you must be able to reconstruct exactly why.

Build the guardrails first and the intelligence second. That order is what makes AI a durable operational advantage instead of an expensive experiment.