How to debug LLM multi-agent systems?
Interactive Debugging and Steering of Multi-Agent AI Systems
March 5, 2025
https://arxiv.org/pdf/2503.02068This paper introduces AGDEBUGGER, a tool for debugging multi-agent AI systems. It addresses the challenges developers face when building and debugging these systems, such as understanding complex agent interactions and the lack of tools for interactive debugging.
Key points for LLM-based multi-agent systems:
- Interactive Debugging: AGDEBUGGER lets developers step through agent messages, send new messages, reset to earlier points in conversations, and edit messages to test different scenarios. This allows for more controlled and insightful debugging than simply reading logs.
- State Checkpointing: AGDEBUGGER checkpoints agent state before each message, allowing for resets that restore agents to previous states. This is crucial for iterative debugging and testing hypotheses.
- Conversation Visualization: An overview visualization helps developers understand long, complex agent conversations and track changes made through edits, aiding comprehension and debugging.
- Agent Configuration: AGDEBUGGER supports basic agent configuration, allowing developers to tweak agent prompts and models while debugging.
- Focus on Interactions: The tool emphasizes understanding and controlling the interactions between LLM-powered agents, addressing a key challenge in multi-agent systems development.
- Steering Agent Behavior: The study shows developers primarily use AGDEBUGGER to add specific instructions, simplify existing instructions, or modify the overall agent plan – highlighting common failure points and debugging strategies for LLM-based agents.
- Challenges Remain: The research highlights the need for improved support for non-resettable actions, better ways to track the effects of edits, and automatic error identification. These are open challenges for the future of multi-agent AI debugging.