Research
PLANNED
The Coherence Trap
Multi-Agent Orchestration Collapse
What It Is
A formal characterization of how multi-agent AI systems collapse into self-reinforcing consensus — and the four metrics that predict it.
The Problem
Multi-agent systems are supposed to generate diverse, independent perspectives. In practice, they converge on shared mistakes and amplify them. This paper names the failure mode, formalizes it, and proves when it occurs.
Approach
- Defined four coherence metrics measuring inter-agent output divergence over time
- Proved three theorems characterizing necessary and sufficient conditions for trap entry
- Analyzed collapse in production-scale multi-agent deployments
- Designed architectural interventions that break self-reinforcing consensus loops
Results
- Formal proof: coherence traps are inevitable without explicit anti-coherence mechanisms
- Four predictive metrics validated on real multi-agent runs
- Architectural interventions measurably delay trap onset
Stack
PythonMulti-agent frameworksStatistical analysisFormal methods