Bad Agents Means Bad Management. Period.
July 23, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
Venture Beat, a quite unusual online publication, published “Enterprise AI Is Entering An Evaluation Gap: Agents Are Gaining Autonomy Faster Than Companies Can Verify Them. ” Is this a write up in the old Amazing Tales magazine or a serious analysis of people who are looking for a way to cut expenses, increase revenue, and land a big bonus payday? The answer: The article is a business and technical analysis prepared by a hard working professional with a track record of covering fast-changing subjects. Therefore, let’s take a look at the write up.
The good news is that software agents armed with “intelligence” haven’t become fully autonomous… yet. What is happening is that enterprise AI teams are giving their AI agents more freedom as their confidence in automated testing is collapsing. Is this one of those inconvenient paradoxes?
About 50% of enterprises have deployed an AI agent or LLM that passed internal evaluations, but they are still failing on the customer facing side. The logical thing to do would be to slow automation but 66% of respondents to a VB Pulse survey said they deployed some production without human oversight. Autonomy is coming sooner than humans are reviewing it. With layoffs and employees dropping out of “real” work, I am not sure that there are enough motivated, informed humans to review agentic software. In my experience, generating code really fast is a core semi-competency.
The article states:
“It also fits a broader thesis that will be explored at VB Transform 2026: enterprises ship agents first, while the control layers around identity, evaluation, cost, context and orchestration are arriving later. The next year will be a retrofit cycle, with buyers shifting budget toward the systems that make agentic deployments governable and dependable.”
Is an agent ready to be turned loose because it completes a task in one successful run? Sure it does. The goal of AI is to achieve repeatable results so that customers don’t experience any hiccups on their end. Every failure should be a regression test to make the AI agent better. Somehow American Airlines garbled by email and displayed an old phone number for me. I called the special number for AAdvantage members. I told the AI agent I needed to update my account. The system did not understand “update my account.” After five repeats telling me to state what I wanted to do, the smart software said, “If you want us to call you in 45 minutes, press one.” I did. In fact, I went through this drill several times. Did the smart software agent get someone to call me? Nope. I wrote a handwritten letter to the leadership of American Airlines, but I don’t think anyone will contact me. The agentic system worked at least once. Therefore, the system is perfect for paying customers.
I don’t think there are enough human hours to review every AI agent action, but:
“w-risk actions such as drafting internal summaries or categorizing documents can tolerate broader autonomy. Financial transactions, customer communications, code deployment, access-control changes and data deletion need stricter thresholds, repeated consistency tests, policy checks, rollback mechanisms and clear human escalation paths. The risk isn’t evenly distributed by company size, either. Larger enterprises — those with 2,500 or more employees — are moving toward zero-human deployment fastest, at 70% versus 64% for smaller companies, and they’re also shipping more agents that go on to fail a customer, at 54% versus 48%.”
I think this means that management must manage agents. The customer centric enterprise teams need to get their acts together and create an evaluation system so bad agents are not foisted on paying customers. Will that happen, Venture Beat?
Whitney Grace, July 23, 2026
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