Different Evals for Agentic AI: Methods, Metrics & Best Practices

Agentic AI represents a significant evolution in artificial intelligence, advancing from merely generating text to autonomously executing multi-step tasks with minimal human intervention. Unlike traditional AI models, agentic AI systems are designed to perceive their environment, form plans, take action, and self-correct, functioning as dedicated digital employees rather than sophisticated calculators. They require a distinct set of evaluation techniques due to their non-deterministic and probabilistic nature, which makes conventional software testing methods inadequate. Evaluating agentic AI involves both outcome-based and trajectory-based assessments, focusing not only on task completion but also on the decision-making process and resilience to errors. This includes using automated tools and human oversight to ensure reliability and safety, especially in high-stakes domains. The complex architecture of agentic AI systems involves key components like a reasoning engine, memory, a tool belt for interaction, and an execution loop for control. Effective testing frameworks leverage AI-assisted tools to assess these systems' external behavior, tool usage, robustness, and ability to self-correct in dynamic environments, ensuring that the agents deliver consistent business value while maintaining compliance and safety standards.