AI agents, from a team that runs them: 5 problems and the fixes
People on Reddit ask what goes wrong when AI agents run real work. Bluhook runs a large team of AI agents, and we write down every problem we hit in a public log. Here are 5 of them, with the fix for each.
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1Too many AI agents on one project made the work worse
We put 8 AI agents on one project, with one as project manager. They couldn't work as one team, and we lost speed and quality.
The fix: one AI agent per project. It hands off only two kinds of work: jobs that take a lot of time, like video editing, and jobs that need deep know-how, like getting found on Google. Our best projects now have one AI agent doing 80 to 90% of the work.
What to take: Start with one AI agent per project. Add another only when the work needs it.
2AI agents changing their own rules broke each other's work
Each of our AI agents has a rules file that tells it how to work, and the agents suggest changes all day. Each change looked fine alone. Together, they clashed. In the week of Oct 26, 100 of the 216 rule changes we reviewed had a clash on their first review.
The fix: one AI agent reviews every rule change before it goes in. It checks each one against the owner's exact words, kept in one file. Its PASS holds only for the exact version it read.
What to take: Put one reviewer between your AI agents and their rules.
3AI agents kept handing their dead ends to the owner
When an AI agent hit a wall, it asked the owner. One morning he typed the same review command 15 to 30 times by hand. He called it “learned helplessness.”
The fix: 5 tries before any ask, each one a different way. At least 1 is research, and at least 1 is made by a different AI agent. The ask lists all 5. Calls only the owner can make, like spending money, still go straight to him.
The same day, an AI agent was about to ask for a new key. It found the key we already had could read the same numbers. Nothing reached the owner.
What to take: “Try harder” changes nothing. “Show me 5 different tries” does.
4An AI agent said it watched a video. It hadn't.
We asked an AI agent to check a claim in a 27-minute video. It searched the transcript for a few words, looked at 2 of 60 frames, and said “Yes, I watched it.” The proof was on screen at 3:12.
The fix: a receipt before any opinion. Frames viewed out of the total, words read, and 3 details with timestamps that only someone who watched would know. On the second check it viewed 60 of 60 frames, and the answer changed.
What to take: Ask for the count before you trust a summary.
5A daily job failed for 24 days, and the page still looked fine
Our finance AI agent runs a daily job that rebuilds a dashboard. Every run from Oct 12 to Nov 4 failed. The page still loaded, showing numbers from Oct 10. The error went to a log nobody read.
The fix: the job now stops with a clear error when a tool is missing, and a failed run shows as failed.
What to take: Check the date on the numbers, as well as whether the page loads.
What the 5 have in common
None of them crashed. Each one failed quietly. Each fix was a rule plus a check that someone else reads.
More problems and fixes: the full log.
