TraceGraph: Shared Decision Landscapes for Diagnosing and Improving Agent Trajectories
Learn to improve agent trajectories using TraceGraph, a graph-based framework that turns agent trajectories into shared decision landscapes, enabling better evaluation and diagnosis of agent performance
- Build a graph over observable action-observation states from pooled rollouts
- Overlay outcome-informed projections onto the graph
- Analyze the graph to identify decision-making patterns and areas for improvement
- Apply TraceGraph to multiple models and tasks to compare performance
- Configure TraceGraph to accommodate different types of agent trajectories and tasks
Researchers and developers working on agent benchmarks and multi-model agent trajectories can benefit from TraceGraph to improve evaluation and diagnosis of agent performance. This can also help AI engineers and data scientists to identify areas for improvement in agent decision-making
💡 TraceGraph enables the creation of shared decision landscapes from agent trajectories, facilitating better evaluation and improvement of agent performance
🤖 Improve agent performance with TraceGraph, a graph-based framework for evaluating and diagnosing agent trajectories! 💡
Key Takeaways
Learn to improve agent trajectories using TraceGraph, a graph-based framework that turns agent trajectories into shared decision landscapes, enabling better evaluation and diagnosis of agent performance
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