The Top of the AI Agent Hype Cycle is Here

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Hello everyone and welcome to my newsletter where I discuss real-world skills needed for the top data jobs and specifically the AI Agent Role. 👏

This week we discuss why we are nearing the top of the agent hype cycle.

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The hype cycle has AI agents perched right at the peak. The demos are mesmerizing: watching an AI system chain together complex tasks feels like witnessing magic. At least, until you try to deploy one on a Tuesday morning while everyone’s waiting for their quarterly reports.

No, this doesn’t mean I’ve changed my mind on the transformational power of agents in the real-world, it does mean I’m always realistic about what the hype is and what the real-world does.

Here’s what the demos leave out: agents are brilliant right up until they aren’t. They’re like that overachieving friend who volunteers to plan the entire office party but forgets to book the venue. Long chains of tasks create long chains of failure points, and when an agent goes off course, it doesn’t just make a small error—it makes a systematic one, with confidence.

The smart money isn’t betting against agents — it’s betting on bounded autonomy. Think of it as giving your AI a really good job description, clear goals, well-defined guardrails, and assigning someone to keep an eye on things — instead of just saying go figure it out. Less AI Chief of Staff, more AI intern who’s really good at spreadsheets but doesn’t get the big picture. Most AI, despite wild marketing claims (aka ‘the hype’), have a long way to go before grasping nuanced complexities and layered contexts.

While everyone’s getting excited about flashy AI agents, the real revolution is happening in the most unsexy corner of tech: data management. Gartner calls it AI-ready data, but I call it finally treating your data like it matters.

Here’s the uncomfortable truth: most AI projects don’t fail because the model can’t think — they fail because the data can’t talk. It’s like trying to have a conversation with someone who’s speaking through a broken telephone while standing in a wind tunnel. Sure, you might catch a word here and there, but you’re not getting anywhere meaningful.

The companies that are actually succeeding aren’t the ones with the fanciest models; they’re the ones who can prove their data is fit for purpose. They know where it came from, who can use it, how fresh it is, and whether it’s telling the truth. It’s not glamorous work, but neither is plumbing — until your pipes burst.

I’ve seen this movie before. Remember when everyone was going to be working in virtual reality by now? Or when blockchain was going to revolutionize everything except actual useful things? This pattern repeats: polished demons, ambitious predictions, massive investments. But making things function in the real world is the long, slow work of rewiring organizational cultures and structures, a process that takes a lot of time.

The thing is, that slow work is where the real magic happens. It’s in the unglamorous process of figuring out data lineage and access permissions. Not to mention cleaning up data and cybersecurity approvals for enterprise systems. It’s in the boring meetings about security protocols and rollback procedures. It’s in the patient work of redesigning workflows so humans and AI can actually collaborate instead of just coexist.

After watching enough hype cycles come and go, I’ve learned the importance of answering these three simple questions:

Number one. What problems are you looking to solve? Not How can AI transform our business? but How can we reduce the time it takes Sarah from accounting to process expense reports? Get specific or you’ll get lost in the sea of ever-changing AI currents.

Number two. Can you measure it? If you can’t put a number on the problem today, you won’t be able to prove you’ve solved it tomorrow. The CFO doesn’t care about your transformational AI journey — they care about whether your solution moves the needle on metrics they already track. Yes, this is an innovation barrier, but most AI projects need a solid proof of concept before scaling.

Number three: What happens when things break? Let’s face it, that disclaimer about AI making mistakes is real. Expect things to break. Not if… rather, when. The companies that succeed are the ones who plan for failure from day one and plan for contingencies. They have rollback procedures, human oversight, and clear escalation paths. They deploy AI as the powerful but imperfect tool it is, rather than a magic wand or silver bullet.

Here’s what I’ve learned from riding multiple hype cycles: the real curve isn’t about technology adoption — it’s about organizational learning.

First comes the excitement phase: This changes everything! Then the reality phase: Wait, this is harder than we thought. Then the wisdom phase: Oh, I see how this actually fits into our world.

Forget about riding the curve. Instead, pick one small, measurable problem and solve it properly. Get your data house in order. Build the boring infrastructure that lets you sleep at night. And when someone asks for a transformational AI vision, offer them something better: a number that moved in the right direction.

Thanks for reading and have a great day. 👏