Code-Based Scorers for AI Agent Evaluation

When building AI agents, relying on manual testing or unpredictable LLM-as-a-judge evaluations is not enough to guarantee production-ready software. To build reliable systems, you need automated, deterministic checks that run in milliseconds. This text-based course guides you through the process of designing and implementing code-based scorers that programmatically validate structured AI outputs. You will learn how to write precise scripts that inspect agent responses, verify data schemas, and score the correctness of complex outputs like coordinates, JSON payloads, and element properties. By establishing these automated guardrails, you can confidently iterate on your prompts and models without breaking existing functionality. What you'll learn: - Understand the fundamentals of AI evaluation, including the difference between heuristic, model-based, and code-based scorers. - Build validation scripts to verify structured data formats, checking for essential keys, types, and value constraints. - Apply modern Python type-hinting and Pydantic schemas to parse and validate LLM outputs automatically. - Create custom scoring metrics to evaluate the structural integrity of complex outputs such as diagram elements and nested coordinates. - Practice writing assertion-based tests to integrate into your automated AI testing pipeline. This course starts with foundational concepts of AI engineering evaluations before guiding you through practical, text-based code exercises. It is designed for beginner AI developers and software engineers looking to make their LLM applications robust and predictable. No prior experience with advanced machine learning is required. Start building reliable, automated evaluation pipelines for your AI agents today.