Phase 3: Scale, Failure & Evaluation · ~75 minutes · Python (stdlib)

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.

Hiring signal: Can operate failure modes in production

Introduction

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.

Type: Learn Languages: Python (stdlib) Prerequisites: Phase 16 · 13 (Shared Memory), Phase 16 · 14 (Consensus and BFT), Phase 16 · 15 (Voting and Debate Topology) Time: ~75 minutes

Objective

Learning objectives

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Key terms in this lesson

Agent