Paper Reference
- Title: Interactive Debugging and Steering of Multi-Agent AI Systems
- Authors: W Epperson, G Bansal, VC Dibia, A Fourney, et al.
- Year: 2025
- URL: https://dl.acm.org/doi/
- Venue: ACM Conference on Human Factors in Computing Systems (CHI)
Paper Summary
Explores interfaces for debugging increasingly complex multi-agent AI systems with focus on effective support for debugging multi-agent teams. Identifies key challenges: coordination failures, emergent behavior, and communication breakdowns between agents.
Proposed Feature
Implement a multi-agent swimlane debugger:
Core Capabilities
- Swimlane View: Display each agent as a horizontal lane with its actions plotted temporally
- Message Flow Arrows: Show inter-agent communication as arrows between lanes
- Coordination Analysis: Detect coordination failures, deadlocks, and communication gaps
- Emergent Behavior Detection: Identify behaviors that emerge from agent interactions but aren't attributable to any single agent
Technical Approach
- Add multi-agent session support to the SDK with per-agent event streams
- Implement swimlane visualization component with D3.js
- Add inter-agent message tracking and visualization
- Build coordination analysis algorithms
Impact
Essential for users debugging multi-agent systems (CrewAI, AutoGen, etc.). Currently no open-source debugger provides this capability.
Labels
enhancement, paper-inspired, frontend, multi-agent
Paper Reference
Paper Summary
Explores interfaces for debugging increasingly complex multi-agent AI systems with focus on effective support for debugging multi-agent teams. Identifies key challenges: coordination failures, emergent behavior, and communication breakdowns between agents.
Proposed Feature
Implement a multi-agent swimlane debugger:
Core Capabilities
Technical Approach
Impact
Essential for users debugging multi-agent systems (CrewAI, AutoGen, etc.). Currently no open-source debugger provides this capability.
Labels
enhancement, paper-inspired, frontend, multi-agent