Resilient Consensus in Agentic AI
Large language model (LLM) agents are increasingly deployed in multi-agent systems where they must coordinate and agree on shared decisions. We ask whether classical resilient consensus theory, developed for deterministic agents, transfers to LLM agents that may behave adversarially. Framing LLM agreement as a Byzantine consensus game, we run controlled experiments on complete and general communication graphs. We find that prompted LLM agents fail to reach agreement that is achievable in principle: consensus can fail even in settings where classical theory guarantees that a convergent algorithm exists, and this failure persists across temperatures and horizons. At the same time, wrapping ...
| Authors | Sribalaji C. Anand, George J. Pappas |
|---|---|
| Primary category | cs.MA |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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