Failure Modes — MAST, Groupthink, Monoculture, Cascading Errors

The reference taxonomy for 2026 is **MAST** (Cemri et al., NeurIPS 2025, arXiv:2503.13657), derived from 1642 execution traces across 7 state-of-the-art open-source MAS showing **41–86.7% failure rate**. Three root categories: **Specification Problems** (41.77%) — role ambiguity, unclear task definitions; **Coordination Failures** (36.94%) — communication breakdowns, state desync; **Verification Gaps** (21.30%) — missing validation, absent quality checks. The **Groupthink** family (arXiv:2508.05687) adds: monoculture collapse (same base model → correlated failures), conformity bias (agents reinforce each other's errors), deficient theory of mind, mixed-motive dynamics, cascading reliability failures. Cascading example: retry storms where a payment failure triggers order retries, which trigger inventory retries, which overwhelm inventory service (10x load in seconds — needs circuit breakers). Memory poisoning: one agent's hallucination enters shared memory, downstream agents treat it as fact; accuracy decays gradually, making root-cause diagnosis painful. **STRATUS** (NeurIPS 2025) reports 1.5x mitigation-success improvement via specialized detection / diagnosis / validation agents. This lesson treats failure modes as first-class engineering targets.

Learn 1.3 hr Python (stdlib)

Prerequisites: Phase 16 · 13 (Shared Memory), Phase 16 · 14 (Consensus and BFT), Phase 16 · 15 (Voting and Debate Topology)

#Problem

Multi-agent systems fail 41-86.7% of the time on real tasks (Cemri et al. 2025 measured this across 7 open-source MAS). That is not debuggable by "just add more agents." The failures have structural causes. The MAST taxonomy gives you the categories. This lesson maps each category to a concrete detection, diagnosis, and mitigation pattern so the numbers stop looking arbitrary.

The 2026 production practice is to treat failure modes as design inputs. Your architecture is not "good enough" until you can point to each MAST category and name the mitigation you deployed.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.