Cadre

Capstone 10 — Multi-Agent Software Engineering Team

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 converged on the same 2026 shape: an architect plans, N coders work in parallel worktrees, a reviewer gates, a tester verifies. 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.

Capstone 40 hr Python / TypeScript (agents), Shell (worktree scripts)

Prerequisites: Phase 11 (LLM engineering), Phase 13 (tools), Phase 14 (agents), Phase 15 (autonomous), Phase 16 (multi-agent), Phase 17 (infrastructure)

#Problem

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.

#Problem

Keep going. The rest of this lesson is one step away.