In the context of AI Vibe Coding, how does Eval Driven Development (EDD) apply to building and refining infrastructure automation scripts and tools?

Question

Tyler Smith · Accepted Answer

Eval Driven Development (EDD), a core philosophy for improving AI products, is highly applicable to building and refining infrastructure automation scripts and tools within an AI Vibe Coding framework. Just as Debugging AI Agents & LLM Applications advocates for continuous iteration in AI development, EDD for infrastructure automation means treating each script, configuration, or automated workflow as an 'AI product' that needs constant evaluation and improvement.

The process begins with 'Unit Tests' for individual automation components, such as a script to provision a server or configure a network device. These tests, fast and cheap, provide immediate feedback on functionality and correctness. As changes are made or new features added, these unit tests are the first line of defense. Next, 'Human & Model Eval' comes into play. Human experts review the outputs and behavior of the automation, while AI Vibe Coding can deploy models to evaluate the efficiency, security, and adherence to best practices of the automated actions. For example, an AI might analyze log data post-automation to detect unexpected behavior or security vulnerabilities.

Finally, 'A/B Testing' can be implemented for critical infrastructure automations. Rolling out two versions of a deployment script to different, isolated environments and monitoring their performance allows for objective comparison and data-driven decision-making. 'Failure modes' are critically examined, just as in AI agent debugging, to understand why an automation failed, leading to updates in the evaluation suite. By embedding EDD, AI Vibe Coding ensures that infrastructure automation scripts are not static but continuously evolve, becoming more robust, efficient, and aligned with desired operational outcomes and EOS goals.