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Evaluating Multi-Agent Systems Beyond the Final Answer
Learn how to evaluate multi-agent AI systems beyond just the final answer. This talk details a framework capturing agent collaboration, tool use, and failure points for better system design.
I am building an evaluation framework for multi-agent AI systems that compares not only whether the system gets the right final answer, but how the agents collaborate to get there. In the live demo, I will show a small multi-agent workflow with a planner agent, worker agents, and a critic/evaluator agent, then walk through the code, traces, tool calls, handoffs, failure cases, and scoring logic used to evaluate each run.
The framework captures the full agent trajectory: which agent took control, what tools were called, where context was passed or lost, how much the run cost, how long it took, and whether the final outcome matched the task goal. I will demo how the same task behaves under different setups, such as a single-agent baseline, a manager-agent architecture, and a multi-agent handoff design.
- PythonPython: The high-level, general-purpose language built for readability, powering everything from web backends to advanced machine learning models.Python is the high-level, general-purpose language prioritizing clear, readable syntax (via significant indentation), ensuring rapid development for any team . Its ecosystem is massive: use it for robust web development with frameworks like Django and Flask, or leverage its power in data science with libraries such as Pandas and NumPy . The Python Package Index (PyPI) provides thousands of community-contributed modules, offering immediate solutions for tasks from network programming to GUI creation . The language is actively maintained by the Python Software Foundation (PSF), with the stable release currently at Python 3.14.0 (as of November 2025) .
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- CrewAIThe open-source Python framework for orchestrating autonomous, collaborative AI agent teams.CrewAI is a lean, lightning-fast Python framework designed for building and deploying multi-agent systems. It empowers developers to define specialized Agents—each with a clear role, goal, and backstory—and organize them into a Crew to tackle complex tasks. The framework manages collaboration via context sharing and delegation, utilizing defined Processes like sequential or hierarchical execution to ensure efficient workflow. Agents gain specific capabilities through integrated features, including access to hundreds of open-source tools (e.g., internet search) and a sophisticated memory management system for short-term and long-term context. You define the agents and tasks; CrewAI handles the orchestration, delivering reliable, scalable AI automation.
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