Agents are useful only when they are bounded by real workflows, explicit tools, and operational checks. The writing here treats agents as production systems, not autonomous magic.
An agent is production-ready only when the system around it is boring:
For orchestration decisions:
For architecture boundaries:
For governance and reliability:
Reliable agents aren't prompted into existence. They're engineered -- with bounded tools, validation at every step, explicit recovery paths, and the same discipline you'd apply to any production system. Here's how I build them in Go.
The trick to AI workflow automation is simple: let the model decide, let deterministic code act, and never confuse the two.
Multi-agent systems aren't magic. They're distributed systems with all the usual coordination headaches. Here are the four patterns I've seen work, and when each one falls apart.
Single-prompt agents break on real tasks. Plan-execute-replan, orchestrated specialists, structured memory, and explicit recovery -- in Go -- are what actually works.
AI agents that can take actions are fundamentally different from chatbots. The engineering bar must match the blast radius.
Most agent demos are impressive. Most agent production systems are not. Here is what separates the two.