# agents

Al Grigor uses Claude Code as an orchestrator across four roles—product manager, software engineer, QA tester, and on-call engineer. Each task must move from backlog through acceptance before commit, avoiding the trap of one agent writing code and approving its own work.

A one-person company in 2026: one person on strategy, configured agents on execution — ~$300/month replacing an $80–120k/month team. The how-to: a Company OS plus four agents — research, writing, sales, ops. On 'works while you sleep' — gu-log runs like this, so we know what the fine print says.

Fable 5 nails on the first try what used to take days — but it's too proactive and over-elaborates, so prompts tuned for Opus 4.8 hold it back. The official guide isn't about making it stronger; it's about reining it in: steer with intent, draw boundaries, talk like a human when the run ends.

When an agent breaks in production, observability hands you a gorgeous autopsy — every call, latency, and token, but not why it broke or how to fix it. The fix is a loop that runs itself: failure → approved patch → locked-in regression test. Opik is just the example; the point is the loop.

LangChain's conceptual guide breaks down agent improvement into a trace-centric loop: collect traces, enrich them with evals and human annotations, diagnose failure patterns, fix based on observed behavior, validate with offline eval, then deploy — each cycle starting from higher ground.