Evaluation & Monitoring
≈ Integration Testing / Observability / APM (Datadog/New Relic)
> Agentic Definition
Frameworks for measuring agent performance (accuracy, faithfulness, tool usage) and monitoring behavior in production (traces, logs).
> Description
Frameworks for measuring agent performance (accuracy, faithfulness, tool usage) and monitoring behavior in production (traces, logs).
≈ How It Maps to Integration Testing / Observability
Tracking system health and correctness.
≠ Key Divergence
"Correctness" is subjective. Traditional metrics (Latency, Error Rate) are insufficient. You need "LLM-as-a-Judge" metrics to score "Correctness," "Hallucination Rate," and "Tone."
> Key Takeaway
Adapt: Testing is no longer binary. It is statistical. You are managing "Quality Assurance" via AI judges.
The Code
Before: Unit Test Assertion
1# Unit Test Assertion2assert function(2, 2) == 4After: LLM-based Evaluation
1# LLM-as-a-Judge2score = evaluator_llm.grade(3 input=question,4 output=agent_answer,5 ground_truth=expected_answer6)7# Returns a score (e.g., 0.85) and reasoning8assert score > 0.9Production Notes
- Continuous evaluation in production is required to detect "drift" (model behavior changing over time due to updates or data changes).
Frequently Asked Questions
When should I use the Evaluation & Monitoring pattern?
Frameworks for measuring agent performance (accuracy, faithfulness, tool usage) and monitoring behavior in production (traces, logs).
How does Evaluation & Monitoring relate to Integration Testing / Observability / APM (Datadog/New Relic)?
Tracking system health and correctness. However, there is a key divergence: "Correctness" is subjective. Traditional metrics (Latency, Error Rate) are insufficient. You need "LLM-as-a-Judge" metrics to score "Correctness," "Hallucination Rate," and "Tone."
What are the production trade-offs of Evaluation & Monitoring?
Continuous evaluation in production is required to detect "drift" (model behavior changing over time due to updates or data changes).