Skill Guide

Evaluation and testing: automated evals for agent accuracy, tool-call correctness, hallucination detection, and regression testing

The practice of systematically measuring an AI agent's performance by automating the verification of its output accuracy, the correctness of its interactions with external tools, the prevalence of fabricated information, and the stability of its behavior across software iterations.

This skill is critical for deploying reliable, production-grade AI agents, directly impacting user trust, system safety, and operational costs by preventing costly errors and ensuring consistent service quality.

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How to Learn Evaluation and testing: automated evals for agent accuracy, tool-call correctness, hallucination detection, and regression testing

Start by understanding the core failure modes: hallucination (agent invents facts), tool-call errors (wrong API, malformed parameters), and regression (performance degrades after an update). Learn to create simple, deterministic test cases with known ground-truth answers and expected tool outputs. Use basic unit testing frameworks (like `pytest`) to run these checks.

Move beyond single test cases to building comprehensive eval suites. Implement metrics like precision/recall for accuracy, success rate for tool calls, and automated fact-checking against a knowledge base. Integrate these evals into CI/CD pipelines so every code change is automatically validated. Learn to use logging and tracing to diagnose failures.

Architect multi-layered evaluation systems. Design synthetic data generators for stress testing, implement human-in-the-loop evals for nuanced quality assessment, and build regression dashboards. Develop custom LLM-as-a-judge evaluators for subjective tasks. Align eval metrics with business KPIs and create feedback loops for continuous model improvement.

Practice Projects

Beginner

Project

Build a Regression Test Suite for a Q&A Bot

Scenario

You have a simple agent that answers questions from a document. You need to ensure a model or prompt update doesn't break its core functionality.

How to Execute

1. Create a JSON file with 20 question-answer pairs from the document (the 'golden' dataset). 2. Write a Python script that uses your agent's API to answer each question. 3. Use a library like `difflib` or an exact match to compare the agent's output to the expected answer. 4. Run the script automatically before and after any changes; fail the build if accuracy drops below 95%.

Intermediate

Project

Automated Tool-Call Validation for a Code-Generation Agent

Scenario

An agent that writes and executes Python code against a data analysis API. You must ensure it calls the correct functions with valid parameters.

How to Execute

1. Define expected API call schemas (e.g., `create_chart(data_source, chart_type, x_axis, y_axis)`). 2. Use a mocking library (e.g., `unittest.mock`) to intercept the agent's API calls. 3. Write tests that assert the agent called the mock with the exact function name and parameter types/values from your test scenario. 4. Integrate these mocked tests into your deployment pipeline.

Advanced

Project

Implement a Hallucination Detection and Scoring Pipeline

Scenario

A customer-facing agent providing medical or financial information must have near-zero hallucination. You need a scalable, automated way to flag and score potential fabrications.

How to Execute

1. Build a knowledge base (KB) of verified facts from trusted sources. 2. For each agent response, use an NLP model to extract key claims (e.g., 'Drug X treats condition Y'). 3. Design a pipeline to cross-reference each claim against the KB, checking for contradictions or unsupported statements. 4. Calculate a hallucination score per response (e.g., % of unsupported claims). 5. Set up alerts for responses exceeding a threshold and feed high-hallucination examples back into the training/fine-tuning cycle.

Tools & Frameworks

Software & Platforms

DeepEvalLangSmith / LangChain EvaluatorsOpenAI Evalspytest / unittestEvidently AI

DeepEval and LangSmith provide dedicated frameworks for LLM evals (accuracy, hallucination, bias). OpenAI Evals offers templates. pytest is for building custom, deterministic test harnesses. Evidently AI is for data and model monitoring in production.

Mental Models & Methodologies

CI/CD for ML PipelinesHuman-in-the-Loop (HITL) EvaluationSynthetic Data GenerationLLM-as-a-Judge

CI/CD pipelines (e.g., GitHub Actions) automate eval runs on every commit. HITL combines automated metrics with human review for quality. Synthetic data creates edge-case test scenarios. LLM-as-a-judge uses a stronger model to evaluate weaker ones for subjective tasks.

Interview Questions

Answer Strategy

Structure the answer around the three core pillars: accuracy (does the summary capture key facts?), tool-correctness (if it accesses a CRM, does it do so properly?), and hallucination (does the reply invent ticket details?). Mention specific metrics (ROUGE, factual consistency score, API call success rate) and how to create a gold-standard test set. Emphasize integration into the development lifecycle.

Answer Strategy

This tests systematic problem-solving. The strategy is to isolate the failure: Is it global or specific to certain inputs? Use logging and tracing to pinpoint where the performance degrades. Check for data drift in the eval set itself. Show a methodical, data-driven approach.