Your AI Agent Isn’t Broken. You Shipped It Early.

Last updated: September 2026

Key Takeaways

• Testing an AI agent is the step almost everyone skips, and most abandoned agents were one round of feedback away from working.
• Build, test, tweak, deploy. The same order that governs software development applies without modification to AI.
• My Instagram reels skill took thirty minutes and three rounds of testing before it worked. That was the entire gap between using it weekly and abandoning it.
• Test as the person who will receive the output, not as the person who built it. That is where handoffs break.

This post is part of my complete guide to why AI isn’t working in your business, and how to fix it.
Read the full guide → Why AI Isn’t Working in Your Business

Your AI agent is probably not broken. You shipped it too early.

Here is what happens. Somebody gets excited, builds the thing, turns it on, walks away. Three weeks later there is a pile of output that is not right, and they have concluded AI cannot do what they wanted.

It could. It just could not do it on attempt one, because nothing does.

I spent forty-plus years in systems development in my corporate life. Software, IT, large builds. Building with AI follows the same rules, in the same order, with the same discipline. Nothing about this is new. People are skipping it because AI feels like it should not need any.

This post covers why everybody skips the testing phase, the four steps that fix it, and the one test almost nobody runs.

Why Testing an AI Agent Gets Skipped

Testing an AI agent means running it on real work, telling it specifically what was wrong with the output, and running it again until the result is something you would have been happy to produce yourself.

Almost nobody does this, and the reason has nothing to do with laziness.

Every client I work with wants to install it and go live the same afternoon. The excitement is real. You have a custom agent, or a whole AI team, sitting right there. Why would you not turn it on?

Because you built it inside your own head, with your own assumptions, and none of them have been checked yet.

In software nobody would do this. You would never write code on a Tuesday and push it to customers on the Tuesday. There would be a testing phase, and somebody whose entire job is finding what you missed.

What makes AI different is that the output looks finished. It is articulate. It is formatted. It reads as though it is done.

That is what fools people. Bad code crashes and you know instantly. A poorly configured agent hands you a clean-looking paragraph that is quietly wrong, and nothing about the presentation tells you which one you are holding.

I have never shipped something to a client without testing it first. Not once in forty years, and not now. That is not caution for its own sake. It is knowing what round one looks like: always close, never right.

Excitement is the real risk here. The more pleased you are with what you built, the less carefully you will look at it.

Build, Test, Tweak, Deploy

Four words governed every system I worked on for four decades, and they apply to AI without modification.

1. Build. Get the first version out. Rough is fine. Rough is expected.
2. Test. Run it on real work. Not a made-up example. Actual work you need done today, because test examples get written easy without you noticing.
3. Tweak. Tell it what was wrong, specifically. Not a request to do better. What exactly missed, and what it should have done instead.
4. Deploy. Only now, when it has produced something you would have been happy to hand over yourself.

The arrow that matters runs backwards. Tweak returns to test, not forward to deploy, and you will usually go around more than once.

Here is a live example with real numbers on it. I have been building my Instagram presence with trial reels and needed a way to do it at scale so I could test my messaging properly. So I built a skill that writes the content the way my coach specifies, then pushes the approved version into a document and over into Canva, into the reel. Captions sitting there ready. I add the video, a few more steps, it is out.

It did not arrive like that. The first runs were not good. The writing was not matching what my coach had specified, then things stopped reaching Canva at all and the handoff was dropping.

Thirty minutes and three rounds of testing fixed it. Now it runs every week.

Thirty minutes stood between a skill I use constantly and a skill I would have abandoned while calling AI useless. Most people quit before those thirty minutes.

Test as the Person Receiving the Output

You test whether it works for you. The test that matters is whether it works for whoever is actually using it.

Most of the time you are not the end user. Your assistant is. A team member is. Somebody who was not inside your head while you built it, who does not have your context, does not know what you meant, and only has what comes out.

So when you test, stop being the builder and become that person. Look at the output and ask whether it would make sense to somebody seeing it for the first time, and whether they would know what to do with it.

This caught something for me hours before a handoff. I had built a skill builder, ready to hand to my assistant, and I ran one last deployment test before sending it over.

It was renaming the agents. We go in and rewrite some of these, and it was not keeping the original agent name. It was assigning its own.

To me, barely noticeable. I know what everything is, because I built it. To her, she would have opened it and had no idea which agent this was, where to deploy it, where to paste it, or how to use it at all.

One small thing, and the whole handoff falls apart. She would have come back confused, and I would have wondered why the skill was not working when the skill was fine and the naming was the problem.

It works for you because you know what you meant. Nobody else gets to know what you meant.

What To Do Next

Run this pass before anything goes live or goes to a team member. Budget thirty minutes, which is what mine took.

1. Run it on real work. Something you need done today. Made-up examples hide problems because you write them easier than reality without realizing.
2. Check every handoff. Anywhere output moves from one place to another is where things drop. Watch it actually arrive rather than assuming it did.
3. Read the output as the person receiving it. Would they know what this is and what to do next, with none of your context?
4. Go around again. Tweak, then test again. Three rounds is a reasonable expectation, and twice is the minimum.

The first step is the real work test. Most problems surface there, and the ones that do not show up in the handoff check.

If you’d like to see which agents are worth building and testing first, take the free 2-Minute AI Business Audit below. It shows you the first three agents to build, what not having them is costing you, and what changes once they’re running.

Round One Is Never the One

Build, test, tweak, deploy. Four steps, four decades old, and they apply to AI exactly the way they applied to software.

My reels skill took thirty minutes and three rounds. My skill builder had a naming problem I would never have caught if I had tested it as myself rather than as my assistant.

Testing an AI agent is not about being slow. It is about looking at each step and each output from somebody else’s chair. Skipping it is one of the four reasons AI isn’t working in most businesses.

Round one is never the one. That is not you doing it wrong. That is how building works. Go and run it one more time. Take the free 2-Minute AI Business Audit, or steal my AI org chart, and see which seats your AI team still has empty.

Related: Why AI Isn’t Working in Your Business: The 4 Real Reasons • He Wanted to Fire His VA. She Wasn’t the Problem.