Agents coverage in this archive spans 7 posts from Sep 2023 to Apr 2026 and treats agents as a production discipline: evaluation loops, tool boundaries, escalation paths, and cost control. The strongest adjacent threads are ai, go, and architecture. Recurring title motifs include agent, ai, patterns, and production.
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.