How to Test AI Agents Before Production

A developer warns that AI agents often fail not because of the model but due to undefined success criteria. The developer recommends a structured evaluation process with regression testing, tracking metrics like tool selection, parameter accuracy, error handling, and cost, to catch failures before production. A free starter kit is offered to help teams implement these checks.

Most AI agents are not failing because the model is useless. They fail because nobody defined what “working” means. A chatbot can answer a question and still fail the actual workflow. An agent can call a tool and still use the wrong parameter. A model upgrade can look better in a demo but silently break your most important use case. This is why vibe-testing is dangerous. If you are building agentic AI workflows, you need a small evaluation process before you ship. Do not use only happy path examples. Include messy inputs, missing details, tool failures, and tasks where the agent should refuse or ask a follow-up question. 5: Excellent 4: Good 3: Usable with review 2: Poor 1: Failed The exact scale matters less than using the same scale every time. Did it choose the correct tool? Did it include the required parameters? Did it handle tool errors? Did it ask for approval before risky actions? Before changing your system prompt, model, tool descriptions, or memory strategy, save baseline outputs. Then re-run the same tests with the new version. If the new version is worse on core tasks, do not ship it. A simple regression test sheet should track: If you do not want to build this from scratch, I included a ready-to-use Prompt Regression Testing Workbook inside the AI Agent Evaluation Starter Kit. Track input tokens, output tokens, number of model calls, and cost per completed workflow. A reliable agent that costs too much to run is still a product problem. For example: Any critical tool-calling failure blocks release. Any unsafe action without approval blocks release. Average score below 4/5 blocks release. Cost above budget blocks release. Final thought The goal is not to make agents perfect. The goal is to make failures visible before your users find them. I created a small AI Agent Evaluation Starter Kit with checklists, test templates, a regression workbook, and a release gate if you want a faster starting point. Get it here: deevthedev.gumroad.com/l/ai evaluation starter kit