Muktesh Mishra warns accuracy metrics fail complex AI agent evaluations

Data is the bedrock of evaluation

One data set is never enough. To build a robust AI application, you must curate multiple data sets tailored to specific user flows. Start small with synthetic data to validate basic outputs, but quickly transition to a continuous refinement process. This involves labeling data according to application prospects and observing system behavior in real-time to ensure the data remains relevant as the model evolves.

Moving beyond generic similarity scores

A universal metric for AI does not exist. A Retrieval-Augmented Generation system requires measuring retrieval accuracy and usefulness, whereas code generation demands a focus on functional correctness and robustness. When evaluating agents, the focus shifts to trajectory evaluation. We must analyze the specific paths an agent takes to execute a task, rather than just the final output. This includes multi-turn simulations to test how agents handle complex, conversational state changes.

Balancing speed and fidelity at scale

Scaling Evals requires a strategic trade-off between automated speed and human fidelity. While automation provides rapid feedback, certain subjective nuances still require a human in the loop. The most effective strategy involves caching intermediate results to prevent redundant computation and running evaluations frequently through parallel orchestration. Success lies in a cycle of measuring, monitoring, and iterating until the system aligns with business goals.