Phase 2: Coordination & Agreement · ~75 minutes · Python (stdlib)
Consensus and Byzantine Fault Tolerance for Agents
Classical distributed-systems BFT meets stochastic LLMs.
Hiring signal: Can operate consensus and byzantine fault tolerance for agents in production
Introduction
In 2025-2026 three research directions emerged: CP-WBFT (arXiv:2511.10400) weighs each vote by a confidence probe; DecentLLMs (arXiv:2507.14928) goes leaderless with parallel worker proposals and geometric-median aggregation; WBFT (arXiv:2505.05103) combines weighted voting with Hierarchical Structure Clustering to split Core and Edge nodes. The honest empirical result from "Can AI Agents Agree?" (arXiv:2603.01213) is that even scalar agreement is fragile today — a single deceptive agent can compromise a Mixture-of-Agents. BFT is necessary but not sufficient. This lesson builds a minimal BFT protocol, injects three agent-specific attacks (byzantine lie, sycophantic conformity, correlated-error monoculture), and measures how each consensus variant copes.
Type: Learn + Build Languages: Python (stdlib) Prerequisites: Phase 16 · 07 (Society of Mind and Debate), Phase 16 · 13 (Shared Memory) Time: ~75 minutes
Objective
Learning objectives
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