How to observe and optimize LLM agent collaborations?
Beyond Black-Box Benchmarking: Observability, Analytics, and Optimization of Agentic Systems
March 11, 2025
https://arxiv.org/pdf/2503.06745This paper addresses the challenges of evaluating and optimizing the behavior of multi-agent AI systems, particularly those using Large Language Models (LLMs). Traditional "black box" benchmarking is insufficient due to the non-deterministic nature of these systems.
Key points for LLM-based multi-agent systems:
- Variability: Both execution flow and the natural language used in prompts and responses introduce variability and make behavior unpredictable.
- Behavioral Benchmarking: The authors propose shifting from outcome-based metrics to analyzing actual agent behavior, including interactions and decision-making processes.
- Observability and Analytics: A new taxonomy and framework are introduced to improve the observability and analysis of multi-agent systems, focusing on capturing non-deterministic elements.
- ABBench: A novel benchmark dataset is introduced to evaluate agent analytic technologies, enabling developers to compare their analytical tools and methods against ground truth data and assess effectiveness in capturing behavioral nuances of agentic systems.
- Optimization Patterns: Optimization strategies like task decomposition, parallel execution, and task merging are presented to help balance quality, performance, and cost in agentic systems.
- User Study: A study of practitioners validated the challenges and highlights the need for improved tools and methods for understanding, debugging, and optimizing agentic system behavior.