Phronia Counsel

The Agentic AI Pretenders

2026 is the year the market finally learns to tell the experts from the snake oil.

By Howard Holton · Phronia Counsel

The number of people who've reached out selling me their "agentic AI business" who six months ago were selling commercial real estate? More than I have fingers on both hands.

They're not experts. They're not masters. They have nothing worthwhile to sell. It takes decades to develop talent and understand how these things should work.

2026 is when we finally learn to tell the difference.

What this means for the CIO, CTO, and CISO

Most "agentic AI" products you're being sold are rebranded automation with a new label. The vendors don't understand the term any better than their sales decks suggest.

Agentic AI solves exactly two problems, and if your foundational AI isn't working, agentic won't fix it. It's an enhancement layer, not a magic wand.

Your vendor qualification process needs a new filter. Can they explain what agentic actually means and where the term came from? If they can't, they're parroting. Walk away.

The inside perspective

When I was deploying AI systems, I learned that understanding why something works matters more than knowing that it works. Every failed implementation I've seen traces back to someone who didn't understand the problem they were solving.

I've sat through hundreds of vendor presentations over my career. The pattern is always the same. A new buzzword emerges, vendors slap it on existing products, buyers get confused, implementations fail, and everyone blames the technology instead of the comprehension gap.

I watched it happen with "cloud-native." I watched it happen with "zero trust." I watched it happen with "digital transformation." Now I'm watching it happen with "agentic AI."

The vendors who actually understood these concepts built products that worked. The vendors who just adopted the terminology built products that disappointed. The difference was never the technology. It was the depth of understanding behind it.

The outside observation

Now I watch the agentic AI gold rush from the analyst seat. It's a masterclass in marketing over substance.

Most vendors took whatever AI they were already doing and slapped "agentic" on it. They don't know what the term means. They don't know where it came from. They don't know what problem it was designed to solve. They just know it's the hot word, and hot words close deals.

The result is a market flooded with "agentic" products that have nothing to do with the actual concept. Buyers can't tell the difference because the vendors can't explain the difference. Everyone's confused, and confusion benefits the people selling snake oil.

Meanwhile, the vendors who actually understand agentic AI, who built products based on the original research and genuine use cases, are getting drowned out by the noise.

The uncomfortable truth

The term "agentic" has a real, specific meaning. It describes adding a reasoning and orchestration layer on top of a model, so the system can plan, call tools, check its own work, and act toward a goal instead of just answering a prompt.

The point was always improving the quality of AI results and reducing hallucinations.

Done well, that layer measurably improves accuracy and reliability on hard tasks. The gains are real. They're valuable. They're worth paying for.

But here's what everyone misses. Agentic AI is an enhancement layer for AI that's already working. It doesn't fix broken implementations. It doesn't compensate for bad data. It doesn't solve problems you couldn't solve before.

Agentic doesn't help if you couldn't use AI before. If your foundational AI wasn't working, agentic doesn't fix it. It's like adding a turbocharger to an engine that won't start.

What agentic actually solves

Let me be precise about what agentic AI actually does. It solves exactly two problems.

Problem 1: Quality validation. Agentic AI provides a validation layer, a faster version of human-in-the-loop. When output quality is too low, the agentic layer iterates and loops until quality improves. It's effectively a built-in checker that addresses a fundamental reality. Humans are inconsistent and lazy. This is valuable, but it only works if the underlying model can produce quality output with iteration. If the model is fundamentally incapable of the task, no amount of validation loops will fix it.

Problem 2: Context window expansion. This is the more valuable application. Each agent operates within its own context window. Break a large task into pieces, farm them out to agents, and you've essentially created a nearly infinite context window. You're only managing response size in the original window, which smart implementation can reduce to almost nothing, especially if agents respond with success criteria and outputs saved as files like Markdown or JSON. This is genuinely powerful for complex, multi-step tasks. But again, it requires the underlying capability to exist.

That's it. Those are the two problems. Everything else being sold as "agentic" is either a misunderstanding or a lie.

The MCP confusion

People constantly confuse agentic AI with MCP, the Model Context Protocol.

They're completely different things.

MCP is a tool integration framework. Think of it as the AI version of an API. It's instructions for AI on how to use APIs provided by applications. It speeds up integration between AI and external tools.

Agentic AI is about quality improvement and context management. It's about the AI validating and improving its own outputs.

One is about connecting AI to tools. The other is about AI improving its own work.

Conflating them means you don't understand either. And if a vendor confuses them in a sales pitch, they don't know what they're selling you.

The pretender problem

Right now, the market is so new that pretenders can sound like experts.

The commercial real estate agent who pivoted to "AI consulting" six months ago can put together a slide deck that looks professional. They can use the right buzzwords. They can reference the right trends. If you don't know the right questions to ask, they sound credible.

But they have no depth. They've never implemented these systems. They've never watched them fail. They don't understand why certain approaches work and others don't. They're parroting what they've read without the comprehension that comes from experience.

Parrots are adorable. But they don't know anything. They just squawk in patterns that sound like language.

The same is true of most vendors in the agentic AI space right now. They're parroting terminology without understanding. When you ask good questions, questions that require actual comprehension, they give you blank looks. They don't have responses they haven't heard a hundred times and memorized.

Signs you're talking to a pretender

Use this diagnostic when evaluating agentic AI vendors or consultants. If three or more apply, walk away.

What I'd tell my former self

If I had known then what I know now:

I would ask every vendor to explain the research behind their terminology. Not the marketing explanation, the actual research. If they can't cite it, they don't understand it.

I would treat buzzword adoption as a red flag, not a green light. The fastest adopters of new terminology are usually the ones with the least understanding.

I would value implementation experience over certification. Anyone can pass a test. Not everyone has deployed systems and watched them fail.

I would budget for the learning curve, not just the license. The difference between success and failure isn't the tool. It's the investment in understanding how to use it.

Why AI is failing in the enterprise

We keep seeing studies about AI failing in the enterprise. The MIT reports. The Gartner surveys. The endless statistics about failed implementations.

Dig into them. The root cause is almost always the same. Organizations didn't understand the problem they were solving.

They bought AI because AI is hot. They deployed agentic because agentic is the new word. They didn't ask what problem these tools actually solve. They didn't validate whether their use case matched the technology's capability.

They flung mud at the wall and hoped diamonds would emerge. That's neither how mud nor diamonds work.

The most important thing for enterprises is to not waste money and time. Resources are limited. Every dollar spent on snake oil is a dollar not spent on something that works.

We need to start with why. Why do these things matter? Why were they created? What are they actually designed to solve? Only then can we tell reality from marketing, and stop buying things that don't move our businesses forward.

The 2026 separation

2026 is when the separation happens. The hype bubble will cool. The easy money will dry up. The pretenders who raised funds on buzzwords will face the reckoning of actual results. Three specific shifts will occur.

Shift 1: Recognition. The market will recognize that agentic AI isn't one thing. It's many things. And expertise in this space takes time. There are now actual experts in the field, people who've done the research, built the implementations, and learned from failures. The market will learn to identify them and distinguish them from the pretenders. Most people selling agentic AI today are just pretenders. 2026 is when that becomes obvious.

Shift 2: Consolidation. Better support and tooling will emerge. The market will contract as pretenders fail to deliver results. Consistency will increase as actual standards develop. The vendors who understood agentic from the beginning will gain ground. The vendors who just adopted the terminology will lose credibility and customers.

Shift 3: Skill prioritization. Curiosity and problem understanding will become the most in-demand skills. The difference between quality AI output and garbage isn't in the prompt. It's in how much time you've invested in getting the AI to understand the problem at its root. When you ask a question from a foundation of understanding, you get quality answers. When you just type prompts, you get AI slop.

The playbook for navigating the separation

  1. Audit your current "agentic" investments. Review everything you've bought or built that's labeled "agentic." Does it actually implement quality validation or context expansion? Or is it just automation with a new label?
  2. Apply the pretender test to all vendors. Use the diagnostic. If vendors fail three or more criteria, reconsider the relationship. You're paying for expertise you're not receiving.
  3. Validate use cases against actual agentic capabilities. Is your use case about quality improvement or context management? If not, agentic isn't the solution. Find the technology that actually matches your problem.
  4. Invest in people who understand, not just use. The curious employees who dig into how things work will deliver better results than the ones who just prompt and pray. Identify them. Enable them. Promote them.
  5. Build relationships with proven experts now. The real experts will be in high demand when the bubble bursts. Establish relationships before everyone else realizes they need them.

The bottom line

Expertise takes decades to develop. Buzzwords take minutes to adopt. The enterprises that learn to tell the difference will be the ones still standing when the hype bubble bursts.

Ask vendors to explain what agentic actually solves. Not what it could theoretically do. Not what the marketing deck claims. What specific problem does it solve, and what's the research basis for that claim? If they can't reference the original research, they're parroting. Don't pay enterprise software prices for parrot-level comprehension.

I've spent 20 years as a CISO, CIO, and CTO, making the technology decisions that either move an organization forward or lock it into regret. The diamonds formed through hard work will outlast the hype that obscured them. 2026 rewards expertise over marketing.