Session brief
Agent Evals: Finally, With The Map
Overview
This talk introduces a framework for evaluating AI agents, dividing agent evaluation into semantic and behavioral aspects. Semantic evaluation focuses on how an agent's internal representations align with reality, while behavioral evaluation assesses how an agent's actions and tool usage contribute to achieving its goals. Both aspects are further categorized into single-turn and multi-turn scenarios, providing a comprehensive map for understanding and measuring agent performance.
Who should watch
- AI Engineers
- Product Managers
- Builders developing AI agents
- Anyone responsible for ensuring agent reliability and performance in production
- Those looking to establish robust testing and evaluation methodologies for AI systems
Key takeaways
- Agent evaluation can be structured into semantic (representation accuracy) and behavioral (goal achievement through actions) components.
- Both semantic and behavioral evaluations can be analyzed at single-turn (e.g., consistency, tool selection) and multi-turn (e.g., conversation flow, task progression) levels.
- Truthfulness in semantic evaluation is achieved through grounding in data, often via Retrieval Augmented Generation (RAG), while goal achievement in behavioral evaluation is grounded by the tools available to the agent.
- Reasoning traces and chain-of-thought processes are crucial areas for multi-turn semantic evaluation.
- Behavioral evaluation includes assessing instruction following, correct tool selection and usage, output quality from tools, and handling of error situations.
- Multi-step behavioral evaluation focuses on whether an agent's actions converge towards its ultimate goal and the quality of its planning.
- Practical considerations like cost, latency, tracing, debugging, and error management are vital for production-ready agents.
- A distinction exists between offline testing during development and online testing during live agent operation.
Notable quotes
*Agent evaluation is rather art and science and ultimately it is nonetheless required to actually ensure that your agents do what you expect them to do especially when you're launching them in production.*
*The representations are in a sense a special case of tools special case of behaviors.*
*Let's go out there and make our agents measurable controllable and let's make sure they are actually doing our biding and not not rebelling against our ultimate intentions.*
Unofficial community note. Prefer the recording for nuance.