What metrics should we use to evaluate AI agent performance?

Summary

• Evaluate agents across three dimensions — quality, cost, and latency — not a single accuracy number, and measure them consistently from development through production.
• MLflow provides the evaluation framework: MLflow Tracing records every step of an agent's execution to debug issues and build evaluation datasets, and all results, metrics, and traces are logged for A/B testing and monitoring.
• Built-in LLM-judge scorers rate responses on correctness, groundedness in retrieved context, safety, relevance, and retrieval quality, with supporting rationale to help find the root cause of quality issues.
• Custom scorers cover domain-specific needs: guidelines-based scorers, prompt-based judges, deterministic code-based scorers for business logic, and tunable judges aligned with your domain experts.
• The same scorers run in production monitoring on a sample of live traffic, and a Review App collects feedback from domain experts and end users, so quality is tracked continuously.

What metrics should we use to evaluate AI agent performance?

Evaluating an AI agent is not a single accuracy score. Agents are multi-step systems, so effective evaluation spans quality, cost, and latency, and it uses the same metrics in development and in production to catch quality drift over time. On Databricks, MLflow provides the evaluation framework, and Agent Bricks applies the same scorers to production traffic for continuous monitoring.

Why Databricks for evaluating AI agent performance

• Three core dimensions: quality, cost, and latency. Agent Evaluation assesses quality through LLM judges and custom metrics, tracks cost across agent operations, and measures latency from development through production, giving a rounded view rather than one number.
• Tracing for every step. MLflow Tracing records each step of agent inference — LLM calls, retriever calls, and tool invocations — so teams can debug where performance issues occur and turn real interactions into evaluation datasets.
• Built-in LLM judges. Databricks provides LLM judges that rate agent responses on correctness, groundedness in retrieved context, safety, relevance, and retrieval quality. Each judge supplies rationale to help identify the root cause of a quality issue.
• Custom and tunable scorers. For domain-specific needs, teams can define guidelines-based scorers, prompt-based custom judges, and deterministic code-based scorers for business logic or format validation, and can align tunable judges with domain experts so evaluation reflects real requirements.
• Development-to-production consistency. Evaluation results, metrics, and traces are logged to MLflow, enabling A/B testing and audit trails. The same judges and scorers used in development run in production monitoring on a sample of live traffic, so teams can identify and address quality drift with the same yardstick.
• Human feedback in the loop. A Review App and human-labeling workflows let domain experts and end users provide feedback that becomes part of evaluation datasets, keeping automated scoring aligned with expert judgment.

Getting started

• Read the evaluation and monitoring documentation for the end-to-end workflow.
• Review scorers and LLM judges to choose built-in versus custom scorers.
• See Building custom LLM judges for AI agent accuracy for aligning judges to your domain.
• Explore Agent Bricks for building and monitoring agents in production.

FAQs

What metrics matter most for AI agents?

Quality (via LLM judges such as correctness, groundedness, safety, relevance, and retrieval quality), cost, and latency — measured together and tracked from development through production.

What is an LLM judge?

An LLM judge is a model-based scorer that rates an agent's responses on a specific dimension and returns supporting rationale, so teams can see not just the score but why a response scored that way.

How do I keep evaluation consistent in production?

Use the same scorers offline and online: MLflow logs evaluation results and traces, and production monitoring runs the same judges on a sample of live traffic to detect quality drift.