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.
- Create a baseline test set Start with 10 to 30 real tasks your users would ask.
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.
- Score outputs consistently Use a simple 1 to 5 score:
5: Excellent
4: Good
3: Usable with review
2: Poor
1: Failed
The exact scale matters less than using the same scale every time.
- Test tool calling separately An agent can produce a nice final answer while making a bad tool call underneath.
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?
- Run prompt regression tests Every prompt change is a code change.
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:
- Test case
- Baseline output
- New output
- Old score
- New score
- Regression status
- Notes
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 cost per run Agents can become expensive quickly because they perform multiple steps.
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.
- Add a release gate Before production, define what blocks a release.
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