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Module 15 · Page 16 of 20 · 10 sections
Evaluation and Benchmarking in Agentic AI
Evaluation dimensions, metric choice per task type, LLM-as-judge, and a full evaluation pipeline.
Why Evaluation is Important?
- Evaluation helps to measure the quality, reliability and usefulness of an agent.
- Agentic systems are more complex than simple LLMs because they take actions, use tools and interact with the environment.
- We need evaluation to ensure the agent is safe, accurate and aligned with user goals.
- It helps in comparing different models, prompts and design patterns.
Key Evaluation Dimensions
- Accuracy
- Gives correct and relevant answers.
- Task Success
- Completes the intended task successfully.
- Reliability
- Produces consistent results.
- Safety
- Avoids harmful or unsafe actions.
- Groundedness
- Uses correct and verifiable information.
- Efficiency
- Completes tasks with minimal time, tokens and cost.
- User Satisfaction
- Meets user expectations and is helpful in real use cases.
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Types of Evaluation
- Intrinsic Evaluation
- Evaluate model output directly (e.g. correctness, fluency).
- Extrinsic Evaluation
- Evaluate based on real-world tasks and user goals.
- Human Evaluation
- Human experts judge the output.
- Automatic Evaluation
- Use metrics and LLM based evaluation.
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Evaluation Flow (General)
InputPrompt / task
AgentLLM + tools
OutputAnswer / action
ReferenceAnswer / expected result
EvaluationMetrics + Human Review
Score + FeedbackImprovement
Common Evaluation Metrics
- Exact Match
- Checks if the output matches the expected answer.
- Semantic Similarity
- Measures meaning similarity (using embeddings).
- Task Success Rate
- Percentage of tasks completed successfully.
- Hallucination Rate
- Measures incorrect or fake information.
- Tool Use Accuracy
- Correct selection and usage of tools.
- Response Time
- Time taken to complete the task.
- Token Usage
- Number of tokens used (cost efficiency).
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Example Metrics for Different Tasks
| Task type | Evaluation metrics |
|---|---|
| Q&A | Exact match, semantic similarity, groundedness |
| Information search | Relevance, correctness, source citation |
| Tool usage | Tool selection accuracy, task success rate |
| Multi-step task | End-to-end success, intermediate step accuracy |
| Code generation | Functional correctness, compilation success, test case pass rate |
| Summarization | ROUGE, BLEU, human feedback |
| Reasoning | Correctness, logical consistency, faithfulness |
Human Evaluation Guidelines
- Use clear evaluation criteria.
- Provide multiple test cases.
- Compare with reference answers where possible.
- Check helpfulness, correctness and safety.
- Use multiple human reviewers to reduce bias.
- Give scores (e.g. 1 to 5) for each dimension.
LLM as a Judge (Automatic Evaluation)
- Use another LLM to evaluate the output.
- Provide evaluation criteria in the prompt.
- Useful for large scale evaluation.
- Still validate with human review for important cases.
prompt
You are an evaluator. Compare the following output with the expected answer and give a score from 1 to 5 based on correctness, relevance and helpfulness. User Query: {query} Expected Answer: {reference} Model Output: {output} Give score and short explanation.
Evaluation Pipeline (Detailed)
DatasetTest cases
Run AgentMultiple configurations
Collect ResultsOutputs, logs
EvaluateAuto metrics + human review
AnalyzeCompare & identify improvement areas
Final ReportScores, charts, insights
Key Takeaways
- 1Evaluation is essential for building reliable and useful agents.
- 2Use a combination of automatic metrics and human review.
- 3Choose the right metrics based on the task type.
- 4Continuously evaluate and improve the agent.
- 5A good evaluation pipeline helps in real-world deployment.