Phase 3: Scale, Failure & Evaluation · ~75 minutes · Python (stdlib)
Evaluation and Coordination Benchmarks
Five 2025-2026 benchmarks cover the multi-agent evaluation space.
Hiring signal: Can operate evaluation and coordination benchmarks in production
Introduction
MultiAgentBench / MARBLE (ACL 2025, arXiv:2503.01935) evaluates star/chain/tree/graph topologies with milestone KPIs; graph is best for research, cognitive planning adds ~3% milestone achievement. COMMA evaluates multimodal asymmetric-information coordination; state-of-the-art models including GPT-4o struggle to beat a random baseline. MedAgentBoard (arXiv:2505.12371) covers four medical task categories and often finds multi-agent does not dominate single-LLM. AgentArch (arXiv:2509.10769) benchmarks enterprise agent architectures combining tool-use + memory + orchestration. SWE-bench Pro (arXiv:2509.16941) has 1865 problems across 41 repos spanning business apps, B2B services, and developer tools; frontier models score ~23% on Pro vs 70%+ on Verified — a reality check on contamination. Claude Opus 4.7 (April 2026) is reported at 64.3% on Pro with explicit agent-teams coordination (no Anthropic primary source published yet — treat as preliminary); Verdent (agent scaffold) hits 76.1% pass@1 on Verified (Verdent technical report). AAAI 2026 Bridge Program WMAC (https://multiagents.org/2026/) is the 2026 community focal point. This lesson builds on MARBLE's metrics, runs a topology-vs-metric sweep, and pins the "just passing SWE-bench Verified is not evidence of generalization" rule.
Type: Learn Languages: Python (stdlib) Prerequisites: Phase 16 · 15 (Voting and Debate Topology), Phase 16 · 23 (Failure Modes) Time: ~75 minutes
Objective
Learning objectives
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