Agentic Workflows: The AI Hype That Might Actually Work

Look, I’ve been in IT long enough to know when we’re being sold a bill of goods. And I’ve also been around long enough to recognize when something genuinely useful is hiding beneath all the marketing noise. Agentic workflows are somewhere in that messy middle ground—and that’s why I find them interesting.

Every few years, a new AI concept shows up and gets the full venture capital treatment. We’ve had our moment with large language models, and now everyone wants to talk about AI agents that can supposedly think, plan, and execute tasks without hand-holding. The financial crowd is sniffing around it too, which typically means either a genuine breakthrough or a very well-timed pump-and-dump scheme. Probably both.

What Agentic Workflows Actually Are

Let me strip away the jargon first. An agentic workflow is basically an AI system that can set goals, break down a problem into steps, execute those steps, and adapt when something goes wrong. Unlike traditional software that follows a predetermined path, agentic systems have some degree of autonomy. They can reason through problems.

Here’s what makes it different from what we’ve had before: a chatbot responds to prompts. An agentic system figures out what it needs to do, gathers information, makes decisions, and reports back when it’s done. It’s the difference between asking someone a question and hiring someone to solve a problem.

In practice, this means:

• Automating entire business processes instead of individual tasks
• Handling unexpected situations without escalating to a human
• Learning from outcomes and adjusting future actions
• Working across multiple systems and data sources autonomously

Does that sound useful? Sure. Does it also sound like the kind of thing that could go catastrophically wrong if implemented badly? Absolutely.

Where This Gets Interesting (and Risky)

I’ve watched enough failed automation projects to know that giving a system more autonomy doesn’t automatically make things better. Sometimes it just means your mistakes happen faster and at scale.

But here’s what’s different this time around. Previous attempts at autonomous systems were brittle. They worked great in controlled environments and fell apart in the real world. Modern AI models, for all their flaws, are actually decent at handling edge cases and ambiguity. That matters.

The companies getting serious about agentic workflows right now aren’t doing it for press releases. They’re doing it because they’ve identified specific problems that autonomous systems can actually solve. A customer service agent that can resolve issues without human intervention. A data analyst that can investigate anomalies and produce reports. A systems administrator that can diagnose and fix certain classes of problems.

These aren’t fantasies. They’re already happening in controlled deployments. The question is whether the technology can mature fast enough to handle more complex scenarios and whether the business case stacks up.

The Stock Angle and Why It Matters

When Cantor Fitzgerald starts talking about some under-the-radar AI company being positioned to skyrocket, that gets people’s attention. And sure, there’s money to be made in emerging technology. But let’s be honest about what that forecast actually means.

It means someone has decided that the company’s current valuation doesn’t reflect their potential upside. That could be right. Or it could be that the stock is being talked up before institutional money positions itself. I’ve seen both happen.

What actually matters for investors isn’t whether agentic workflows are real—they are. It’s whether a specific company can execute on the technology faster than competitors, whether enterprises will actually adopt it, and whether the economics work out. Those are separate questions from whether the technology itself is sound.

As an IT guy, I care about whether this stuff solves real problems. As someone who’s watched tech bubbles inflate, I stay skeptical about 300% predictions. The technology is promising. The business case is compelling in specific scenarios. Whether any particular company captures that value? That’s where the actual risk lives.

The smart move is to understand what agentic workflows actually do, where they’re being deployed successfully, and then make decisions based on fundamentals—not hype cycles. The technology is real. The execution risk is real too. Both matter.

Source: Cantor Fitzgerald Thinks This Little-Known AI Stock Is Set to Soar 300%