Research Paper Analysis
Interactive Debugging and Steering of Multi-Agent AI Systems
Will Epperson (Carnegie Mellon University), Gagan Bansal (Microsoft Research), Victor C Dibia (Microsoft Research), Adam Fourney (Microsoft Research), Jack Gerrits (Microsoft Research), Erkang (Eric) Zhu (Microsoft Research), Saleema Amershi (Microsoft Research)
Abstract: Fully autonomous teams of LLM-powered AI agents are emerging that collaborate to perform complex tasks for users. What challenges do developers face when trying to build and debug these AI agent teams? In formative interviews with five AI agent developers, we identify core challenges: difficulty reviewing long agent conversations to localize errors, lack of support in current tools for interactive debugging, and the need for tool support to iterate on agent configuration. Based on these needs, we developed an interactive multi-agent debugging tool, AGDEBUGGER, with a UI for browsing and sending messages, the ability to edit and reset prior agent messages, and an overview visualization for navigating complex message histories. In a two-part user study with 14 participants, we identify common user strategies for steering agents and highlight the importance of interactive message resets for debugging. Our studies deepen understanding of interfaces for debugging increasingly important agentic workflows.
Key Insights & Executive Impact
Our research reveals critical challenges in multi-agent AI development and introduces AGDEBUGGER to address them.
5
AI Agent Developers Interviewed
14
Participants in User Study
3
Core Debugging Features Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Agent Debugging Challenges
AGDEBUGGER Features
User Strategies & Insights
Core Debugging Challenges
Developers face significant hurdles in building and debugging multi-agent AI systems, primarily due to the complexity of long, multi-turn conversations and the lack of interactive debugging support. Localizing errors within extensive agent logs is a time-consuming process that current tools do not adequately address.
Interactive Debugging Capabilities
AGDEBUGGER introduces novel features including a UI for browsing and sending messages, the ability to edit and reset prior agent messages, and an overview visualization for navigating complex message histories. These features enable users to interactively test hypotheses and steer agent behavior.
Effective Steering Strategies
Our user studies highlight the importance of interactive message resets for debugging. Participants leveraged AGDEBUGGER to reset agents to earlier workflow points and edit messages, demonstrating common strategies for steering agents towards desired outcomes.
Multi-Agent AI Debugging Workflow with AGDEBUGGER
Identify Incorrect Output (e.g., Wrong Output!)
→
Reset Agents to Earlier Workflow Point
→
Edit Agent Messages or Instructions
→
Test Hypotheses & Rerun Workflow
→
Observe New Output (e.g., Correct Output!)
| Feature | Current Tools | AGDEBUGGER |
|---|---|---|
| Long Conversation Review |
|
|
| Interactive Debugging |
|
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| Hypothesis Testing |
|
|
5X
Faster Error Localization in Preliminary Studies (Estimated)
Enhancing AI Agent Development with AGDEBUGGER
The AGDEBUGGER tool was developed to directly address core challenges identified through formative interviews with leading AI agent developers. By providing interactive control and clear workflow visualization, AGDEBUGGER significantly streamlines the process of diagnosing and resolving errors in complex multi-agent systems, moving beyond traditional LLM debugging methods.
This interactive approach empowers developers to achieve robust and reliable AI agent behavior more efficiently, accelerating the development of next-generation autonomous AI systems.
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Phase 01: Discovery & Strategy
Initial consultation to understand your unique business needs, identify high-impact use cases, and define clear objectives for AI agent deployment.
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Develop and test a proof-of-concept AI agent system in a controlled environment. Gather feedback and refine agent behavior based on real-world interactions.
Phase 03: Iterative Development & Integration
Scale the AI agent solution, integrate it with existing enterprise systems, and perform continuous debugging and steering to optimize performance and ensure reliability.
Phase 04: Training & Rollout
Provide comprehensive training for your team, facilitate a smooth organizational rollout, and establish ongoing monitoring and support frameworks.
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