Module 08: Agent Evaluation

The Most Underrated Problem in Agentic AI

You have built an agent. It calls tools, reasons through steps, and produces outputs. The question nobody wants to answer: is it actually good?

Evaluation is where most agentic AI projects quietly fail. Not because agents are bad - but because teams ship without any principled way to measure, compare, or improve them. They add a feature, run a few manual tests, and call it done. Months later, a production incident reveals the agent was subtly wrong for weeks.

Evaluation is not a nice-to-have. It is the engineering discipline that separates research demos from production systems.

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Why Agent Evaluation Is Uniquely Hard

For a classifier, evaluation is straightforward: labeled dataset, accuracy/F1, done.

Agents break every assumption:

• No single ground truth. A user asks the agent to plan a trip to Tokyo. There are thousands of valid itineraries. Which one is correct?
• Multi-step, non-deterministic. The agent makes 15 decisions. Each could go wrong. The final output may be correct even if intermediate steps were wrong.
• Compound errors. A mistake in step 3 propagates through steps 4–15. Attribution is almost impossible.
• Expensive to evaluate. Every agent run costs real tokens, real time, real API calls. Running 1000 eval examples is not free.
• Distribution shift. Your eval set was curated in March. By June, users are asking things you never anticipated.

This module addresses all of it - systematically.

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Module Map

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Key Concepts

Term	Definition
Trajectory	The full ordered sequence of (state, action, observation) tuples an agent produces for one task
Benchmark	A curated, standardized dataset of tasks with scoring methodology for cross-agent comparison
LLM-as-Judge	Using a capable LLM to score or rank another agent's outputs - automated evaluation at scale
Monitoring	Continuous measurement of agent behavior in production, with alerting on regression
Inter-annotator agreement	Statistical measure of how consistently human evaluators rate the same examples
Evaluation pyramid	Unit tests → integration tests → human eval → production monitoring, each layer slower, costlier, more realistic

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Prerequisites

Before this module, you should have completed:

• Module 01 - Agent Foundations (understand what an agent is, what a tool is, what a trajectory is)
• Module 02 - Tools and Function Calling (understand how tool calls work and what can go wrong)
• Module 03 - Memory and Knowledge (understand how context is managed across steps)

You should be comfortable with: Python async/await, basic statistics (mean, standard deviation, correlation), and the Anthropic SDK.

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What You Will Build

By the end of this module, you will have assembled a complete evaluation framework:

1. Evaluation harness - run any agent against a task set and collect trajectories
2. Trajectory scorer - compute 6+ metrics per trajectory automatically
3. LLM-as-judge pipeline - rubric-based and pairwise scoring with calibration
4. Human eval toolkit - annotation interface, inter-annotator agreement calculator
5. Production monitor - distributed tracing, anomaly detection, alerting

These components compose into a flywheel: production data feeds your eval set, eval scores guide improvement, improvements ship to production.

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Lessons in This Module

Lesson	Title	What You Learn
01	Challenges of Evaluating Agents	Why eval is fundamentally hard, the evaluation pyramid, dimensions to measure
02	Trajectory Evaluation	Recording and scoring agent trajectories across 6 quality dimensions
03	GAIA Benchmark	Real-world general-purpose agent benchmark - setup, scoring, SOTA analysis
04	SWE-bench Verified	Gold standard coding benchmark - methodology, failure modes, running locally
05	LLM-as-Agent-Judge	Automated evaluation at scale - rubrics, bias mitigation, calibration
06	Human Evaluation	When and how to run human eval - annotator design, agreement, feedback loops
07	Production Monitoring	Metrics, tracing, anomaly detection, and the production improvement flywheel