An agent that dazzles in a demo can fall apart the moment real users and real stakes arrive. The gap between prototype and production is mostly about the unglamorous parts. Here are six things to get right before you ship.
1. Cap Iterations and Cost
An agent loop can run away — calling tools endlessly, burning tokens and money. Always set a hard ceiling on steps and a budget on token spend per task:
if step_count > MAX_STEPS or tokens_used > MAX_TOKENS: return "Stopped: exceeded limits."
2. Constrain What Tools Can Do
An agent with a tool that deletes records will eventually delete the wrong record. Scope tool permissions tightly, require confirmation for destructive actions, and never give an agent more capability than the task needs.
3. Log Every Step
When an agent misbehaves, you need to see exactly what it decided and why. Log every tool call, its arguments, and its result. Without a trace, production agent bugs are nearly impossible to diagnose:
log.info("tool_call", name=call.name, args=call.args, result=result)
4. Handle Tool Failures Gracefully
Tools fail — APIs time out, services go down. If a failure returns garbage, the agent flails. Return clear, actionable error messages so the agent can adapt or stop cleanly rather than loop.
5. Validate Outputs Before Acting
Never let an agent's raw output trigger a real-world action unchecked. Validate structure and content first — an agent that "confirms a refund" should hit a validation layer before any money moves.
6. Keep a Human in the Loop for High Stakes
For consequential actions — money, data deletion, external messages — require human approval. Full autonomy is appropriate for low-stakes tasks; for high-stakes ones, the agent proposes and a human confirms.
Summary
Production-ready agents need guardrails the demo never showed you: cap iterations and cost, scope tool permissions tightly, log every step, handle tool failures cleanly, validate outputs before they cause real actions, and keep a human in the loop where the stakes are high. The intelligence is the easy part — the safety rails are what let you actually trust an agent with real users.