Responding to the "where do you apply this" question: business operations is the clearest non-coding use case, and it looks very different from software dev tasks.

We run a multi-agent stack (OpenClaw + Claude) for a startup that operates autonomously: checking Stripe for new payments, publishing content via APIs, posting on HN, pushing site updates via GitHub API, monitoring email, generating and scheduling work. No human in the loop between sessions.

The agent architecture that emerged: - Coordinator agent reads state files, decides what needs doing, dispatches - Worker agents execute discrete tasks (write article, fetch data, post comment) - Each worker writes a structured log of what it did and why - Coordinator reads logs next session, adapts based on outcomes

The "self-learning" part we've found valuable isn't in-context learning -- it's persistent state files that capture what worked and what didn't, readable by future sessions. Each agent run adds to a cumulative memory that shapes the next run's priorities.

Failure modes we've hit: 1. Cascading context rot when workers share state (fixed with append-only logs) 2. Agents retry failed actions forever without escalating (fixed with hard retry caps + human flag) 3. Works great in dev, breaks at 3 AM when no one is watching (fixed with audit trail logging)

The application surface is huge for any business with repetitive, API-accessible workflows.