The narrative around agentic AI is intensifying. Enterprises are eager to integrate autonomous capabilities, and vendors are racing to rebrand traditional automation as “AI Agents.” Yet, beneath the surface of this momentum lies a sobering truth: most of what’s being marketed as agentic AI today is neither autonomous nor intelligent.
Gartner projects that by 2028, 33% of enterprise software will include agentic AI, and 15% of routine business decisions will be made autonomously. But a recent finding from same Gartner predicts that by 2027, 40% of agentic AI initiatives will be abandoned—due to high costs, limited returns, and weak governance. Details are here.
If we’re going to succeed with this next leap in AI, we need to separate clarity from chaos—starting with what agentic AI actually is.
Part I: Defining Agentic AI—Digital Labor That Delivers Economic Value
Agentic AI is not just another software layer. It is a fundamental shift in how work is done.
At its core, Agentic AI is digital labor—autonomous or semi-autonomous systems designed to perceive, reason, decide, act, and learn in service of defined goals. These agents operate with minimal human intervention. They move beyond static rules to interpret dynamic data, initiate multi-step tasks, respond to context in real time, collaborate cross boundaries and adapt their strategies based on feedback and evolving priorities.
Think of them not as smarter bots or chat interfaces, but as digital team members with operational capacity. Unlike traditional automation—which focuses on rigid, predefined tasks—agentic systems are built to handle ambiguity, discover workflows, and optimize outcomes over time. They can operate across tools, initiate actions independently, and improve with experience.
When done right, agentic AI becomes a productivity multiplier. It can reduce labor-intensive workloads, compress decision cycles, and enable new business models—driving real economic value at scale. Yet, for this vision to hold, clarity is essential. Gartner outlines key differentiators: true agentic systems possess memory, long-horizon goal orientation, dynamic planning, cross-system execution, and continuous learning. Very few systems meet these requirements today.
Which brings us to the problem at hand.
Part II: The Rise of Hype—and the Misuse of the Agentic Label
What should be a measured, technical transformation has been overtaken by marketing bravado.
Thousands of vendors now claim to offer “agentic AI” when in reality they are repackaging rules engines, robotic process automation (RPA), or scripted chat tools. Gartner identified fewer than 130 vendors globally that meet the baseline criteria for true agent capabilities.
This misrepresentation—now widely referred to as “agent washing”—has become endemic. It’s the AI equivalent of “greenwashing,” where superficial claims mask an absence of real innovation. It enables companies to capture attention, funding, and market share without delivering actual autonomy or intelligence.
The fallout is already visible: unmet expectations, wasted investments, and a growing erosion of trust across AI ecosystems.
Part III: The Forces Behind the Misalignment
Why is this happening? Because the incentives are misaligned—and the pressures are immense.
Capital flows to AI-first companies at a premium. Startups with “AI” in their pitch raise significantly more than those without, regardless of their depth. Public companies mentioning AI in earnings calls see short-term stock boosts. These financial signals create a powerful motivation to exaggerate.
At the same time, many executive teams feel intense pressure to “do something with AI.” Board-level mandates often lack the technical nuance to differentiate agentic capability from automation. The result: projects launched for signaling, not strategy.
Compounding the issue is a widespread misunderstanding of what constitutes intelligence. Many organizations still view deterministic workflows, business logic, or simple decision trees as “AI,” leading to inflated expectations and poor implementation.
Part IV: Regulatory Reality and Responsible Deployment
This widening credibility gap has not gone unnoticed. Regulatory scrutiny is intensifying.
In 2023, the U.S. Federal Trade Commission launched Operation AI Comply to investigate deceptive AI marketing. The Securities and Exchange Commission has also stepped in, with enforcement actions against companies exaggerating AI functionality in investor materials.
Case in point: Presto Automation, which claimed to use AI voice agents in drive-thrus, was found to rely on human operators. The SEC charged the company for misleading investors.
Another example: DoNotPay, promoted as the “world’s first robot lawyer,” faced FTC complaints when it was revealed that the service lacked both AI depth and legal credentials.
These actions are just the beginning. As the AI market matures, so too will regulatory rigor. Companies that make unsubstantiated claims risk not just reputation—but litigation. For buyers, the path forward is due diligence. Understand how a system works, what data fuels it, and whether it truly operates independently. Demand evidence, not roadmaps. Insist on outcomes, not narratives.
For builders, responsible deployment begins with ethical architecture: bias audits, explainable systems, human-in-the-loop safety, and strict governance. Marketing must reflect engineering reality, not aspiration.
Part V: Building Real Value, With Clear Business Outcome
Agentic AI represents a massive opportunity—if done right.
These systems are not just extensions of automation. They are the foundation of self-optimizing enterprise workflows, capable of learning from context, driving outcomes, and continuously improving over time. But their value lies not in their novelty—but in their results. Businesses that succeed with agentic AI will be those who treat it as a disciplined operational capability, not a speculative PR asset.
The winners won’t be the first to shout “AI agent.” They’ll be the first to deploy real digital labor that performs, learns, and scales with the business. Agentic AI isn’t an illusion. But the hype around it is. Let’s cut through it—and build systems that work.
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