Artificial Intelligence
Shipping AI Agents in Production
Hard-won lessons from running multi-agent AI workflows for real businesses — what works, what to avoid, and what's overhyped.
JP Palacio1 min de lectura
AI agents are not magic. In production, they look more like distributed systems with personality. Here is the short version of what I have learned.
Treat agents like microservices
Each agent should do one job well, expose a clear contract, and fail loudly. The "one big assistant" pattern collapses under real workloads.
Ground everything in business context
Agents are only useful when they have access to the documents, data, and APIs the work actually happens in. The model is the smallest part of the stack.
Measure cost and latency from day one
Token cost and tail latency are real product metrics. Bake them into your dashboards before you ship.