Building Production-Ready AI Agents

AI agents promise to automate complex workflows, but getting them production-ready requires more than wrapping an LLM in a loop.

Start with bounded tasks

The most reliable agents do one thing well: classify support tickets, extract data from invoices, or route requests to the right team. Broad "do anything" agents fail unpredictably.

Tool design matters

Every tool your agent can call should have clear inputs, outputs, and error handling. Ambiguous tools lead to ambiguous behavior.

Human-in-the-loop by default

Design for review and override. Agents should propose actions; humans approve high-stakes decisions until trust is established.

Observability is non-negotiable

Log every tool call, LLM request, and decision path. When something goes wrong — and it will — you need a trace to debug it.

Key takeaway

Production agents are systems engineering problems, not prompt engineering problems. Invest in infrastructure, monitoring, and guardrails from day one.