Agent Evaluation with MLflow

Comprehensive guide for evaluating GenAI agents with MLflow. Use this skill for the complete evaluation workflow or individual components - tracing setup, environment configuration, dataset creation, scorer definition, or evaluation execution. Each section can be used independently based on your needs.

⛔ CRITICAL: Must Use MLflow APIs

DO NOT create custom evaluation frameworks. You MUST use MLflow's native APIs:

• Datasets: Use mlflow.genai.datasets.create_dataset() - NOT custom test case files
• Scorers: Use mlflow.genai.scorers and mlflow.genai.judges.make_judge() - NOT custom scorer functions
• Evaluation: Use mlflow.genai.evaluate() - NOT custom evaluation loops
• Scripts: Use the provided scripts/ directory templates - NOT custom evaluation/ directories

Why? MLflow tracks everything (datasets, scorers, traces, results) in the experiment. Custom frameworks bypass this and lose all observability.

If you're tempted to create evaluation/eval_dataset.py or similar custom files, STOP. Use scripts/create_dataset_template.py instead.

Table of Contents

1. Quick Start
2. Documentation Access Protocol
3. Setup Overview
4. Evaluation Workflow
5. References

Quick Start

⚠️ REMINDER: Use MLflow APIs from this skill. Do not create custom evaluation frameworks.

Setup (prerequisite): Install MLflow 3.8+, configure environment, integrate tracing

Evaluation workflow in 4 steps (each uses MLflow APIs):

1. Understand: Run agent, inspect traces, understand purpose
2. Scorers: Select and register scorers for quality criteria
3. Dataset: ALWAYS discover existing datasets first, only create new if needed
4. Evaluate: Run agent on dataset, apply scorers, analyze results

Command Conventions

Always use uv run for MLflow and Python commands:

uv run mlflow --version          # MLflow CLI commands
uv run python scripts/xxx.py     # Python script execution
uv run python -c "..."           # Python one-liners

This ensures commands run in the correct environment with proper dependencies.

CRITICAL: Separate stderr from stdout when capturing CLI output:

When saving CLI command output to files for parsing (JSON, CSV, etc.), always redirect stderr separately to avoid mixing logs with structured data:

# Save both separately for debugging
uv run mlflow traces evaluate ... --output json > results.json 2> evaluation.log

Documentation Access Protocol

All MLflow documentation must be accessed through llms.txt:

1. Start at: https://mlflow.org/docs/latest/llms.txt
2. Query llms.txt for your topic with specific prompt
3. If llms.txt references another doc, use WebFetch with that URL
4. Do not use WebSearch - use WebFetch with llms.txt first

This applies to all steps, especially:

• Dataset creation (read GenAI dataset docs from llms.txt)
• Scorer registration (check MLflow docs for scorer APIs)
• Evaluation execution (understand mlflow.genai.evaluate API)

Discovering Agent Structure

Each project has unique structure. Use dynamic exploration instead of assumptions:

Find Agent Entry Points

# Search for main agent functions
grep -r "def.*agent" . --include="*.py"
grep -r "def (run|stream|handle|process)" . --include="*.py"

# Check common locations
ls main.py app.py src/*/agent.py 2>/dev/null

# Look for API routes
grep -r "@app\.(get|post)" . --include="*.py"  # FastAPI/Flask
grep -r "def.*route" . --include="*.py"

Understand Project Structure

# Check entry points in package config
cat pyproject.toml setup.py 2>/dev/null | grep -A 5 "scripts\|entry_points"

# Read project documentation
cat README.md docs/*.md 2>/dev/null | head -100

# Explore main directories
ls -la src/ app/ agent/ 2>/dev/null

Setup Overview

Pre-check: Use Existing Environment

Before doing ANY setup, check if MLFLOW_TRACKING_URI and MLFLOW_EXPERIMENT_ID are already set:

echo "MLFLOW_TRACKING_URI=$MLFLOW_TRACKING_URI"
echo "MLFLOW_EXPERIMENT_ID=$MLFLOW_EXPERIMENT_ID"

If BOTH are already set, skip Steps 1-2 entirely. The environment is pre-configured. Do NOT run setup_mlflow.py, do NOT create a .env file, do NOT override these values. Go directly to Step 3 (tracing integration) and the evaluation workflow.

Setup Steps (only if environment is NOT pre-configured)

1. Install MLflow (version >=3.8.0)
2. Configure environment (tracking URI and experiment)
   • Guide: Follow references/setup-guide.md Steps 1-2
3. Integrate tracing (autolog and @mlflow.trace decorators)
   • ⚠️ MANDATORY: Use the instrumenting-with-mlflow-tracing skill for tracing setup
   • ✓ VERIFY: Run scripts/validate_tracing_runtime.py after implementing

⚠️ Tracing must work before evaluation. If tracing fails, stop and troubleshoot.

Checkpoint - verify before proceeding:

• MLflow >=3.8.0 installed
• MLFLOW_TRACKING_URI and MLFLOW_EXPERIMENT_ID set
• Autolog enabled and @mlflow.trace decorators added
• Test run creates a trace (verify trace ID is not None)

Validation scripts:

uv run python scripts/validate_environment.py  # Check MLflow install, env vars, connectivity
uv run python scripts/validate_auth.py         # Test authentication before expensive operations

Evaluation Workflow

Step 1: Understand Agent Purpose

1. Invoke agent with sample input
2. Inspect MLflow trace (especially LLM prompts describing agent purpose)
3. Print your understanding and ask user for verification
4. Wait for confirmation before proceeding

Step 2: Define Quality Scorers

1. Check registered scorers in your experiment:

   uv run mlflow scorers list -x $MLFLOW_EXPERIMENT_ID
   

IMPORTANT: if there are registered scorers in the experiment then they must be used for evaluation.

2. Select additional built-in scorers that apply to the agent

See references/scorers.md for the built-in scorers. Select any that are useful for assessing the agent's quality and that are not already registered.

3. Create additional custom scorers as needed

If needed, create additional scorers using the make_judge() API. See references/scorers.md on how to create custom scorers and references/scorers-constraints.md for best practices.

4. REQUIRED: Register new scorers before evaluation using Python API:

   from mlflow.genai.judges import make_judge
   from mlflow.genai.scorers import BuiltinScorerName
   import os
   
   scorer = make_judge(...)  # Or, scorer = BuiltinScorerName()
   scorer.register()
   

** IMPORTANT: See references/scorers.md → "Model Selection for Scorers" to configure the model parameter of scorers before registration.

⚠️ Scorers MUST be registered before evaluation. Inline scorers that aren't registered won't appear in mlflow scorers list and won't be reusable.

5. Verify registration:

   uv run mlflow scorers list -x $MLFLOW_EXPERIMENT_ID  # Should show your scorers
   

Step 3: Prepare Evaluation Dataset

ALWAYS discover existing datasets first to prevent duplicate work:

1. Run dataset discovery (mandatory):

   uv run python scripts/list_datasets.py  # Lists, compares, recommends datasets
   uv run python scripts/list_datasets.py --format json  # Machine-readable output
   uv run python scripts/list_datasets.py --help  # All options
   

2. Present findings to user:

   • Show all discovered datasets with their characteristics (size, topics covered)
   • If datasets found, highlight most relevant options based on agent type

3. Ask user about existing datasets:

   • "I found [N] existing evaluation dataset(s). Do you want to use one of these? (y/n)"
   • If yes: Ask which dataset to use and record the dataset name
   • If no: Proceed to step 4

4. Create new dataset only if user declined existing ones:

   # Generates dataset creation script from test cases file
   uv run python scripts/create_dataset_template.py --test-cases-file test_cases.txt
   uv run python scripts/create_dataset_template.py --help  # See all options
   

   Generated code uses mlflow.genai.datasets APIs - review and execute the script.

IMPORTANT: Do not skip dataset discovery. Always run list_datasets.py first, even if you plan to create a new dataset. This prevents duplicate work and ensures users are aware of existing evaluation datasets.

For complete dataset guide: See references/dataset-preparation.md

Checkpoint - verify before proceeding:

• Scorers have been registered
• Dataset has been created

Step 4: Run Evaluation

1. Generate and run evaluation script:

   # Generate evaluation script (specify module and entry point)
   uv run python scripts/run_evaluation_template.py \
     --module mlflow_agent.agent \
     --entry-point run_agent
   
   # Review the generated script, then execute it
   uv run python run_agent_evaluation.py
   

   The generated script creates a wrapper function that:

   • Accepts keyword arguments matching the dataset's input keys
   • Provides any additional arguments the agent needs (like llm_provider)
   • Runs mlflow.genai.evaluate(data=df, predict_fn=wrapper, scorers=registered_scorers)
   • Saves results to evaluation_results.csv

⚠️ CRITICAL: wrapper Signature Must Match Dataset Input Keys

MLflow calls predict_fn(**inputs) - it unpacks the inputs dict as keyword arguments.

Common Mistake (WRONG):

def wrapper(inputs):  # ❌ WRONG - inputs is NOT a dict
    return agent(inputs["query"])

2. Analyze results:

   # Pattern detection, failure analysis, recommendations
   uv run python scripts/analyze_results.py evaluation_results.csv
   

   Generates evaluation_report.md with pass rates and improvement suggestions.

References

Detailed guides in references/ (load as needed):

• setup-guide.md - Environment setup (MLflow install, tracking URI configuration)
• Tracing: Use the instrumenting-with-mlflow-tracing skill (authoritative guide for autolog, decorators, session tracking, verification)
• dataset-preparation.md - Dataset schema, APIs, creation, Unity Catalog
• scorers.md - Built-in vs custom scorers, registration, testing
• scorers-constraints.md - CLI requirements for custom scorers (yes/no format, templates)
• troubleshooting.md - Common errors by phase with solutions

Scripts are self-documenting - run with --help for usage details.