Research Report 5.3: Coordination & Conflict Resolution
When multiple AI agents disagree - and they will - the resolution mechanism you chose determines whether your system self-corrects or spirals into confident nonsense
Research into how multi-agent LLM systems detect disagreements, resolve conflicting outputs, and maintain coordination under failure conditions - covering voting protocols, authority hierarchies, consensus algorithms, deadlock prevention, and quality control mechanisms with real framework implementations.
Also connected to
The invisible nervous system connecting AI agents determines whether your multi-agent system scales or collapses under its own coordination overhead
Splitting a complex task across multiple agents sounds straightforward until you realize that how you decompose the work determines whether your agents collaborate or collide
The agent that turns plans into code - executing task by task, committing atomically, handling deviations on the fly, and producing a traceable record of every decision made during implementation
GSD agent that creates executable phase plans with clear objectives, tasks, and success...
The agent that treats every bug as a scientific experiment - forming falsifiable hypotheses, testing one variable at a time, and fighting its own cognitive biases to find root causes