The Operon LibraryChapters 11 Published 11
Volume VI · The future isn't one AI. It's coordinated systems — used sparingly.
Multi-Agent Development
The volume leads with the contrarian evidence: the vendor with the most successful production multi-agent system argues most coding tasks parallelize poorly. From there it covers the economics of parallelism, the orchestrator–worker pattern, worktree isolation, context sharing, conflict resolution, and judge panels at fleet scale, closing with a framework for designing agent systems.
Key sources: Anthropic "How we built our multi-agent research system" · MCP/A2A protocol survey literature · worktree-orchestration practice guides · Cursor experiment writeups
Related
- Run multiple AI coding agents in parallelIsolated Git worktrees and one Mission Control grid for every session.
- Compare AI coding agents on the same taskRace two to four agents on one goal and judge them on cost and diff quality.
- The AI-assisted software engineering glossaryEvery term this volume uses, with coinages marked and origins cited.
- The Library’s research sourcesThe dated evidence base behind every claim in the handbook.