How Evaluation-Driven Development (EDD) Works

2026-07-02 · Source: Comet · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

Evaluation-Driven Development (EDD) is a method to detect regressions and measure performance before merging code changes in AI agents, addressing silent failures and the challenge of evaluating new features without historical data. Implemented by Alejandro Aboy for the Workpath AI Companion, EDD utilizes two modes: a quick manual check for minor prompt tweaks (Mode 1, ~30 traces) and automated experiments for new functionality (Mode 2). The process involves using Claude Code to simulate fresh, diverse traces based on agent source code and historical data, running them through a headless agent with production-proxied context injected at the system-prompt level. Opik, an AI observability and evaluation platform, manages evaluation datasets, static code metrics, and LLM judges, enabling comparison of experiment runs. The approach emphasizes offline validation to avoid the high costs of online evaluations, which can reach around \$2k per month. The EDD skill and headless harness are available as an open-source Claude Code plugin.

Key takeaway

For AI Engineers developing or maintaining AI agents, adopting an Evaluation-Driven Development (EDD) workflow is crucial to prevent silent regressions and validate new features efficiently. Implement offline validation using simulated traces and dedicated evaluators to catch issues before merging, significantly reducing the risk of unexpected production failures and controlling evaluation costs. Consider integrating open-source tools like Opik and Claude Code to streamline this process and ensure robust agent performance.

Key insights

Evaluation-Driven Development (EDD) uses simulated traces and offline experiments to validate AI agent changes and prevent regressions.

Principles

• Silent AI failures are common and dangerous.
• Compare agent performance before and after changes.
• LLM judges should be binary classifiers with critiques.

Method

EDD involves defining a hypothesis, simulating fresh traces using Claude Code, running them through a headless agent with mocked system-prompt context, and evaluating results via Opik experiments with static code and LLM judges.

In practice

• Simulate inputs, not outputs, for realistic traces.
• Mock agent state at the system-prompt level.
• Prioritize offline evaluations to manage costs.

Topics

• AI Agent Evaluation
• Evaluation-Driven Development
• AI Observability
• Regression Testing
• Prompt Engineering
• Opik
• Claude Code

Code references

• comet-ml/opik
• aboyalejandro/eval-driven-development

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Comet.