How I automate agent evals starter kit for AI agent workflows

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Evaluating AI Agents: A Developer's Starter Kit

The Problem Developers Face

As developers, we’re increasingly integrating AI agents into our workflows, whether for automating tasks, building conversational bots, or creating intelligent systems. But here’s the catch: once you’ve built an AI agent, how do you know it’s actually working as intended? Sure, it might generate responses or complete tasks, but is it doing so reliably, accurately, and in a way that aligns with your goals? Evaluating AI agents is a nuanced challenge that goes beyond simple unit tests or manual spot-checking.

The problem gets even trickier when you’re dealing with large language models like OpenAI’s GPT or Anthropic’s Claude. These models are probabilistic, meaning their outputs can vary even with the same input. How do you measure performance across different scenarios? How do you identify edge cases? And how do you ensure your agent is improving over time? Without a structured evaluation process, you’re left guessing—and that’s not a great place to be when deploying AI into production.

Common Approaches That Fall Short

Many developers start with manual testing: feeding inputs to the agent and eyeballing the outputs. While this works for quick checks, it doesn’t scale. Others try to repurpose traditional software testing frameworks, but these often lack the flexibility to handle the probabilistic nature of AI. Some teams rely on user feedback as their primary evaluation method, but this is reactive and can lead to costly issues slipping through the cracks. None of these approaches provide the systematic, repeatable evaluation process that AI agents require.

A Better Approach: Structured Agent Evaluation

What if you could evaluate your AI agents systematically, with a framework that’s designed specifically for the challenges of working with language models? That’s where structured agent evaluation comes in. Instead of relying on ad-hoc testing, you define evaluation criteria upfront, create diverse test cases, and measure performance across multiple dimensions. This approach gives you a clear picture of how your agent is performing and where it needs improvement.

A key capability of structured evaluation is scenario-based testing. You create test cases that simulate real-world scenarios your agent will encounter. For example, if you’re building a customer support bot, you might test how it handles angry customers, ambiguous queries, or requests for refunds. Each scenario is evaluated against predefined success criteria, such as response accuracy, tone, and compliance with business rules.

Another important feature is automated scoring. Instead of manually reviewing outputs, you can use scripts to compare the agent’s responses against expected outputs. This might involve exact matches, semantic similarity checks, or even custom scoring functions. Here’s a simple Python example using cosine similarity to evaluate a response:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Expected and actual responses
expected = "The refund process takes 3-5 business days."
actual = "Refunds are processed within 3 to 5 business days."

# Compute similarity
vectorizer = TfidfVectorizer().fit_transform([expected, actual])
similarity = cosine_similarity(vectorizer[0:1], vectorizer[1:2])

print(f"Similarity score: {similarity[0][0]:.2f}")

Finally, structured evaluation supports iterative improvement. By tracking performance metrics over time, you can identify trends, prioritize fixes, and measure the impact of updates. This turns evaluation into a continuous feedback loop, ensuring your agent gets better with every iteration.

Quick Start

Here’s how you can get started with structured agent evaluation:

Define your evaluation criteria: Decide what success looks like for your agent. Is it accuracy, response time, tone, or something else? Be specific.
Create diverse test cases: Write test cases that cover a range of scenarios, including edge cases. Use real-world examples whenever possible.
Automate scoring: Write scripts to evaluate your agent’s responses against expected outputs. Use libraries like sklearn for similarity checks or build custom scoring functions.
Run evaluations regularly: Integrate evaluation into your CI/CD pipeline or run it manually after each update. Track metrics over time.
Analyze and iterate: Review the results, identify areas for improvement, and update your agent. Repeat the process to ensure continuous improvement.