When an AI Says the Job Is Finished:4 Checks I Use After Testing Gemini 4 Argon Against Claude Opus 5.5

If an AI has ever told you a job was finished and it wasn't, a smarter model won't fix that. A second AI checking the first one will, and I found that out testing two of the best models I've used on real work.

New models launch every few weeks, and each one gets called the best. On October 17 I said it myself about Gemini 4 Argon, Google's newest model. Since then I've put it head to head with Claude Opus 5.5 on two real jobs. Here's what happened, and the four checks I use now so a customer isn't the one who finds the bug.

The connector that passed every test

On November 2, Argon built a connector for me. It lets my AI agents message the assistants I keep in Grok Bot, a chat app from xAI, and get their answers back.

Both models let you choose how hard they think. I ran Argon on its high setting, and it built the connector in about 90 minutes: a background service, a small Mac helper app, 14 passing tests, and a live test where the assistant replied "PONG."

Then I asked Claude Opus 5.5, on its medium setting, to check the work. Five minutes later it found a bug that broke the main job.

Step 1: Have a different AI check the work

I open a fresh chat, ideally with a different model, and ask it to find what doesn't work.

The second job showed why. It was a 10-minute video counting down times AI went wrong in 2026. Argon wrote the script. When I handed the project to Opus, it fact-checked that script against the sources. All 8 stories had at least one line the sources didn't support, and one story mixed up two different incidents.

Step 2: Test it the way a customer would

Here's the bug Opus found in the connector. It treated a reply as finished 1.8 seconds after the assistant's latest message.

So an assistant that says "On it" and then works for a few minutes would hand back only "On it." A one-word test like PONG can't catch that, because PONG is the whole answer.

Real requests aren't one word. So I test with a long, messy request that takes a while to answer, the kind a real customer or a real coworker would send.

Step 3: Get a score before you call it done

The checker can fall short too, and I want to be fair to Argon here.

On the video, I gave Argon 13 rounds of notes over two days. It still opened on a looping clip, played one story's footage under another story's narration, and slides crept back in after I asked for them gone.

On a Sunday morning I handed the same project to Opus 5.5. In under two hours it made 8 new versions and had two separate AI reviewers, one of them Google's Gemini, score each one. When the narrator named a company, the real news headline was on screen.

Opus did better, but it didn't hit its own target either. The reviewers scored the final cut 6 and 6.5 out of 10. I called it good enough, and that was my call to make, not the AI's.

That's the point of the score. It doesn't make the decision for me. It tells me what I'm deciding about.

Step 4: Keep a human yes before it goes live

To Argon's credit, it stopped and asked me before installing anything on my Mac. That's the behavior I want everywhere.

I want that pause on anything a customer will see, too: an email, a page on my site, a message that goes out under my name.

My order today

Opus 5.5 is still my daily driver, and Argon builds fast and aims high. What changed for me is that neither one's "finished" counts until something else has checked it.

If you run a business, which AI you pick matters less than who checks its work. You don't need to write code to use these four checks. A second chat window, one realistic test, a score and your own yes are all it takes.

What's one thing an AI told you was finished that turned out not to be?