Capstone 10 — Multi-Agent Software Engineering Team
The 2026 shape of a multi-agent engineering team has converged: an architect plans, N coders work in parallel worktrees, a reviewer gates, a tester verifies. SWE-AF's factory architecture, MetaGPT's role-based prompting, AutoGen 0.4's typed actor graph, Cognition's Devin, and Factory's Droids all landed on it independently. Parallel worktrees convert wall-clock into throughput. Shared state and handoff protocols become the failure surface. The capstone is to build the team, evaluate on SWE-bench Pro, and report which handoffs break and how often. Phases exercised: P11 · P13 · P14 · P15 · P16 · P17 Single-agent coding harnesses hit a ceiling on large tasks. Not because any individual agent is weak, but because a 200k-token context cannot hold an architecture plan plus four parallel codebase slices plus reviewer commentary plus test output. Multi-agent factories split the problem: an architect owns the plan, coders own implementation in parallel worktrees, a reviewer gates, a tester verifies. SWE-AF's "factory" architecture, MetaGPT's roles, AutoGen's typed actor graph — all three framings describe the same shape. The failure surface is the handoff. Architect plans something the coders cannot implement. Coders produce conflicting diffs. Reviewer approves a hallucinated fix. Tester races a still-writing coder. You will build one of these teams, run it on 50 SWE-bench Pro issues, track every handoff, and publish the post-mortem. Roles are typed…
Capstone 10 — Multi-Agent Software Engineering Team: The 2026 shape of a multi-agent engineering team has converged: an architect plans, N coders work in…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
Browse the complete course catalog or open this lesson on GitHub.