The misuse of “Agentic AI” mirrors past cloudwashing
There’s a trend repeating. Years ago, when cloud computing started to get attention, a lot of companies slapped the word “cloud” onto their old infrastructure. Anything hosted remotely was suddenly “cloud-enabled.” It looked innovative on the surface, but the architecture didn’t change. Complexity stayed. Flexibility didn’t improve. Companies invested billions thinking they were upgrading. What they really bought was poor implementation in shiny packaging. And they paid for it, in time and in opportunity.
We’re going down the same road with agentic AI. Today, every vendor is adding the word “agent” to their AI products. But most of what’s out there isn’t actually agentic. It’s scripted workflows or single-output LLMs wrapped in nice interfaces. It doesn’t think, it doesn’t adapt, and it doesn’t act autonomously.
If decision-makers don’t call out the difference early, they’ll sign up for tools that only look advanced. Labeling doesn’t transform the tech. Smart investment starts with understanding what’s real, not with believing what’s marketed.
Defining true agentic AI capabilities
For technology to be called an AI agent, it has to do more than respond with pre-written outputs. Real agentic AI has four defining traits.
First, it pursues goals with autonomy. That means it isn’t following a fixed script. It sets a direction, makes decisions, and moves independently toward outcomes. Second, it handles multistep tasks. It doesn’t just give you an answer, it builds a plan, executes it, adapts, and updates that plan if something changes. Third, it reacts to input and feedback, adjusting behavior in real time, rather than breaking or stalling when it hits something new. Fourth, it takes real action, not just communication. It uses tools, calls APIs, and directly changes environments across software workflows.
If your AI system doesn’t meet those, it’s not an agent. That doesn’t mean it’s useless, but labeling basic orchestration or prompt chaining as autonomous agents is misleading. When you call something agentic, you’re telling teams and decision-makers that it can operate independently, adapt in real time, and reduce workload. If it can’t, the gap between expectation and delivered performance grows fast, and that creates problems at scale.
Taking the time to define these traits isn’t about slowing down innovation. It’s about keeping your architecture clean, your investments sharp, and your talent focused on the right problems.
Strategic, operational, and governance risks from agentwashing
When companies start believing they’ve deployed real AI agents, but haven’t, they open themselves to unnecessary risk. It happens quickly. Executives approve investments assuming the product can operate with minimal human input. Boards validate strategy thinking the organization is moving ahead in AI maturity. Compliance teams trust that controls are adequate for a system with learning capability or autonomous behavior. But if the system is just a deterministic workflow with an LLM behind it, that belief is wrong.
The result is a stack of decisions made on false assumptions. You think you’re buying speed and flexibility, but instead you’re adding maintenance and fragility. Resources go toward tools that need constant supervision. Security and risk teams aren’t given enough information to design proper protections. And when things break, or fail to scale, the signs point back to the initial overstatement of what the platform really is.
The lesson here isn’t to fear new technology. It’s to get clear about what you’re working with. If you’re buying a workflow enhancement tool, treat it like one. If an AI product can’t reason through a changing environment, don’t treat it as autonomous. Strategic alignment starts with understanding what the software actually does, and doesn’t do.
Proactive identification and rebuttal of agentwashing
Spotting agentwashing early prevents poor adoption. In most cases, the warning signs aren’t hard to find. If a vendor talks vaguely about reasoning and autonomy, but underneath it’s just prompt templates and scripts driving everything, that’s a red flag. If most of their system behavior is “orchestrated” around a single LLM call, they’re not running agents, they’re linking prewritten pieces with probabilistic output.
Watch for claims about “fully autonomous” workflows that still require human verification or frequent correction. There’s nothing wrong with keeping people in the process. It’s often necessary. The problem is framing it as something it’s not. That misalignment creates confusion when you’re trying to scale, budget, or measure success.
Executives should push beyond the demo. Expect detailed architecture diagrams, clear failure modes, and measurable outcome claims. Ask how the system decides next steps, handles failure, or adapts to new inputs. If those answers are vague or evasive, walk away. Proof comes from the architecture, not the UI demo or the marketing language.
Reward vendors that tell the truth. Smart automation, even if limited in scope, can be incredibly valuable. But for AI investments to drive core transformation, the governance, expectations, and implementation need to be built on facts, not on hype. Executives who apply this scrutiny early will save resources and avoid scaling the wrong systems.
The imperative for precise language and evidence-based evaluations
Precision in language is strategic. When you call something “agentic,” it sets an internal expectation. Teams start to build around it. Risk assessments shift. Roadmaps lock in. If the label doesn’t fit the technology, the entire system ends up misaligned. You don’t just lose time, you introduce long-term friction across teams and functions.
What works is specificity. Instead of vague claims about “autonomous AI,” get to the point: What can the system do without intervention? What percentage of a workflow is automated? How often does it fail? Where are the limits by design? These are the kinds of details that matter in procurement, in reviews, and in measuring ROI. Contracts should reflect exact functionality, improvement metrics, and decision boundaries, not general ideas of progress.
Leadership teams should expect more than demos. Clear evidence includes architecture flowcharts, planning logic, contingency handling, and accuracy thresholds. It’s not about skepticism. It’s about making strong decisions with verifiable information. If a vendor can’t explain how their system reasons, acts, and recovers when conditions shift, they’re not ready for deployment at scale.
Agentwashing as a governance hazard
Agentwashing isn’t just a branding problem, it’s a governance issue. Whether or not it crosses a legal boundary, the consequence is similar. Decisions are made on inaccurate information. Capital is misallocated. Controls are weakened. In enterprise environments, that’s all it takes to create systemic risk.
Technology oversight today has to move faster and look deeper. Most boards apply more scrutiny to financial forecasting than to AI system behavior, and that has to change. If a product is misrepresented and gets deeply embedded, the cost of correcting course rises significantly. Reverse-engineering secure workflows or re-training teams after false starts isn’t efficient. It’s damage control.
When teams treat agentwashing like they would bad accounting, procurement gets sharper. Deployment criteria improve. System evaluations become structured. This is where real competitive advantage is found, when leadership insists on technical honesty early and avoids being pulled into short-sighted decisions. The businesses that succeed with agentic AI will be the ones that demanded truth before integration.
Key takeaways for decision-makers
- Mislabeling AI mirrors past cloud missteps: Leaders must scrutinize AI terminology now to avoid repeating the strategic and financial waste seen during the early cloud era, where rebranding masked outdated architecture.
- True AI agents require specific capabilities: Clearly differentiate tools from agents, only systems that plan, adapt, and act autonomously should be treated as agentic. This distinction shapes governance scope, risk controls, and ROI expectations.
- Overstated autonomy carries enterprise risk: Decision-makers should challenge inflated vendor claims early to avoid wasted investment, weak compliance posture, and scaling fragile automation as if it were adaptive AI.
- Spot warning signs and demand technical clarity: Leaders must identify agentwashing through vague autonomy promises or one-shot LLM workflows, and demand architecture-level validation before advancing procurement decisions.
- Prioritize precision and measurable outcomes: Avoid vague success metrics tied to “autonomous AI.” Instead, require performance data linked to defined workflows, error tolerance levels, and autonomy boundaries when evaluating tools.
- Treat agentwashing as a governance failure: Overstated capabilities should be reviewed with the same rigor as financial misrepresentation. Tech strategy, risk models, and investments must align with what the systems can actually do.
