Agentic AI or Agentic Hype? Why GRC Buyers Need to Look Past the Marketing
I am increasingly concerned by how loosely the term Agentic AI is being used across the governance, risk management, and compliance market. What should be a meaningful distinction in capability is rapidly becoming a fashionable label applied to almost anything with a prompt, a workflow trigger, or a generative text output. This is not a minor issue of terminology. It is a growing problem in market understanding, buyer expectations, and strategic technology decisions.
The GRC market has always had a tendency to repackage familiar capabilities in the language of the moment. We have seen this with “intelligent,” “predictive,” “cognitive,” “continuous,” and “autonomous.” Today, the favored term is “agentic.” It appears in product messaging, sales presentations, feature announcements, and roadmap conversations with increasing frequency. Yet in many cases, what is being described is not truly agentic AI. It is useful AI, yes. It may even be innovative AI. But that does not make it agentic in the fuller and more meaningful sense.
This matters because organizations are being asked to invest in these capabilities. Boards, executives, risk functions, compliance teams, audit leaders, and technology decision-makers are being told that a new era of intelligent digital workers has arrived. They are being encouraged to believe that their GRC platforms can now reason, act, and adapt in ways that fundamentally transform governance, risk, and compliance operations. In some cases, that promise may eventually be realized. But in far too many cases today, the reality falls well short of the rhetoric.
The Problem with the Label
The term “agentic AI” should imply something substantive. It should refer to AI that does more than simply respond to a prompt or generate content on demand. A genuinely agentic capability should be able to understand an objective, evaluate context, reason through a problem, develop a sequence of actions, use tools or data sources as needed, adapt to changing conditions, and work toward an outcome with some bounded level of autonomy. That is a meaningful step beyond traditional workflow automation or embedded AI assistance.
Yet much of what is being marketed as agentic AI in GRC today looks more like a familiar set of features with new branding layered on top. A system may populate a field with AI-generated text based on other fields in a record. A workflow may trigger an AI-generated summary when a status changes. A chatbot may answer questions from a limited body of content. A recommendation engine may suggest the next step in a process. A rules-driven automation may invoke a language model and present the result in a dashboard, a form, or a case record. These are not without value. In many cases, they are helpful, efficient, and commercially relevant. But they are not the same thing as an autonomous or semi-autonomous agent pursuing an objective across a broader business context.
The issue is not that these features exist. The issue is that the market increasingly collapses all AI-enabled capability into a single term and, in doing so, erodes precision. When everything becomes “agentic,” the word itself begins to lose meaning.
What Often Gets Labeled “Agentic” But Is Not
To be clear, there are many capabilities in the market that are useful and worthwhile but should not be described as agentic AI. Examples include:
- An AI-generated summary in a form, record, or case file that simply turns structured data into narrative text.
- A prompt-based assistant embedded in a workflow step that helps a user draft content, complete a field, or suggest a response.
- A rules-triggered automation that calls an LLM to classify, summarize, or enrich a record when a status changes.
- A chatbot over policies, controls, or regulations that retrieves and answers from a limited corpus but does not act on anything.
- A recommendation engine for next best action that suggests a task or reviewer based on predefined logic.
- An AI-enhanced workflow script that still follows a deterministic process but has generative output inserted into the flow.
- Auto-population of risk or compliance fields based on related record data, templates, or previous entries.
- A single-step task bot that performs one action in a narrow context but does not reason across a broader process or objective.
Again, these can be good capabilities. Some are very good capabilities. But a useful AI feature is not automatically an agent.
Why This Matters So Much in GRC
In many software markets, exaggerated language is annoying but manageable. In GRC, it is more consequential. Governance, risk management, and compliance are not casual administrative domains. They sit at the intersection of strategy, accountability, uncertainty, policy, ethics, internal control, regulatory obligation, and organizational integrity. The systems that support GRC are not simply there to make work faster. They are there to ensure that the organization reliably achieves objectives, addresses uncertainty, and acts with integrity.
That is why accuracy in describing AI capability matters so much.
If a buyer believes they are investing in technology that can coordinate risk analysis across multiple sources, interpret changing regulatory context, plan a sequence of response actions, escalate intelligently, maintain context across cases, and orchestrate work across departments, they are making a strategic architectural decision. If what they are actually buying is a workflow enhancement that generates content in a specific field or recommends the next task from a predefined pattern, then there is a material gap between expectation and reality. That gap will show up in failed transformation initiatives, poor implementation outcomes, misplaced trust, and executive disappointment.
In GRC, poor clarity is not just a product marketing issue. It is a governance issue. Organizations need to know what a system can actually do, where its autonomy begins and ends, how decisions are made, what guardrails are in place, how outputs are verified, how actions are logged, and where human oversight remains essential. Without that clarity, the market risks building castles on buzzwords.
Useful AI Is Not the Same as Agentic AI
Part of the problem is that the market has become uncomfortable with nuance. There is an apparent belief that if a capability is described too precisely, it will sound less exciting. So instead of saying, “this is an AI-assisted workflow feature that summarizes data and populates structured fields,” vendors jump to “agentic AI.” Instead of saying, “this assistant recommends next steps based on configured rules and contextual prompts,” they describe it as an intelligent agent. Instead of saying, “this capability generates outputs inside a defined process,” they imply something much closer to autonomous orchestration.
That kind of inflation helps no one.
There is nothing wrong with AI-assisted workflow features. In fact, many of them are exactly what organizations need right now. They can reduce manual effort, improve consistency, accelerate assessments, support issue management, strengthen policy workflows, help with control documentation, summarize evidence, and enhance user engagement. Those are real benefits. But the value of a capability should stand on its actual merits. It does not need to be elevated into something it is not.
The market should be able to say clearly: this is embedded AI, this is decision support, this is generative assistance, this is rules-driven automation enhanced with AI, and this is an actual agentic capability. Those are different categories. They should not be blurred together simply because “agentic” is currently the most marketable term.
What Truly Agentic Capability Would Look Like in GRC
When I think about real agentic AI in the context of GRC, I am not thinking about a clever chatbot sitting on top of a workflow or a form. I am thinking about a capability that can take an objective such as understanding third-party exposure, coordinating a regulatory change response, maintaining operational resilience, or orchestrating a control review process and then reason across multiple data sources, systems, and decision points to move work forward in a meaningful way.
A truly agentic system in GRC would need to do far more than generate text. It would need to understand objectives in context. It would need to work across processes, not just inside isolated tasks. It would need to manage state, maintain traceability, use tools intentionally, escalate intelligently, adapt when conditions change, and function within clear boundaries of authority and accountability. It would need to know when to act, when to recommend, when to pause, and when to defer to human judgment. It would need to support governance, not bypass it.
In other words, truly agentic AI in GRC would not simply automate pieces of work. It would orchestrate outcomes in alignment with business objectives, risk appetite, policy, and control structure. That is a very high bar. It is also a bar that most currently marketed “agentic” features do not meet.
What True Agentic AI in GRC Might Actually Look Like
To make this practical, examples of genuinely agentic AI in GRC would look more like:
- A third-party risk agent that identifies onboarding requirements, gathers internal and external intelligence, determines inherent risk, requests additional evidence, routes issues to the right stakeholders, tracks responses, and escalates unresolved concerns based on policy and risk appetite.
- A regulatory change agent that monitors changes, interprets relevance to the organization, maps obligations to policies, processes, controls, and business owners, recommends remediation actions, coordinates follow-up, and tracks completion with auditable traceability.
- An operational resilience agent that detects a disruption scenario, identifies impacted services, dependencies, third parties, controls, and obligations, proposes response actions, coordinates tasks across teams, and monitors progress against resilience tolerances.
- A policy governance agent that reviews changes in law, standards, incidents, and control failures, identifies which policies and procedures need revision, drafts proposed updates, routes them for review, tracks approvals, and verifies downstream attestation and training actions.
- An issue and action management agent that evaluates findings from audits, incidents, assessments, and complaints, clusters related issues, proposes remediation plans, coordinates ownership, monitors deadlines, and adjusts escalation paths as new information emerges.
- A control assurance agent that understands the control library, gathers evidence from systems, determines whether testing is needed, adjusts the testing plan based on prior results and risk context, flags exceptions, and coordinates follow-up validation.
These are not simple prompt-and-response features. These are multi-step, context-aware, goal-oriented capabilities operating with bounded autonomy inside a governed framework.
The Questions Buyers Need to Ask
Organizations evaluating GRC technology need to become much more disciplined in how they assess AI claims. They should not be dazzled by terminology or assume that a vendor’s use of the word “agentic” corresponds to a mature capability. Buyers need to interrogate the architecture behind the message.
They need to ask whether the AI is merely generating an output or whether it can actually reason through a sequence of actions. They need to ask whether the capability is confined to a single step in a workflow or whether it can work across a process. They need to ask what tools or systems it can access, how it decides among them, how it handles exceptions, and whether it adapts dynamically to changing context or simply responds to predefined triggers. They need to ask how memory is maintained, how accountability is assigned, how outputs are validated, and what audit trail exists for recommendations and actions. They need to ask where human oversight is required and what governance mechanisms constrain the system’s operation.
These are not peripheral questions. They are central questions. A GRC buyer who cannot answer them does not really understand what they are buying.
Market Clarity Requires Better Discipline
Vendors have every right to innovate. They should continue to push AI forward. The GRC market absolutely needs more intelligent, contextual, and orchestrated capability. It needs systems that reduce fragmentation, connect objectives to risk and compliance activity, and help organizations respond faster and more effectively to uncertainty. AI can and should play a central role in that future.
But the market also needs more discipline in how these capabilities are described.
Not every AI-enabled feature is agentic. Not every automated recommendation is intelligent orchestration. Not every workflow assistant is a digital worker. There is no shame in offering a useful, bounded, practical AI capability. In fact, there is often more value in that than in grand claims of autonomy. What undermines trust is not modest capability. What undermines trust is imprecise positioning.
Analysts need to challenge vague language. Buyers need to demand specificity. Vendors need to be clearer about what their capabilities actually do. If we fail to do that, “agentic AI” will become the next empty phrase in enterprise software, applied so broadly that it signals little and obscures much.
A Call to Action
This is my call to the market.
- Vendors: be precise in your language. Describe what the AI actually does, where it acts, what autonomy it has, and what constraints govern it.
- Buyers: do not purchase the label. Evaluate the architecture, the decision logic, the orchestration capability, the guardrails, and the auditability.
- Analysts and advisors: challenge vague claims and push for meaningful differentiation between AI assistance, AI automation, and true agentic capability.
- Organizations: demand market clarity so strategy, architecture, and investment decisions are grounded in reality rather than hype.
The future of GRC will involve agentic capabilities. I do believe that. But we are not served by pretending that every AI-enhanced feature has already arrived at that future. Precision matters. Integrity in market language matters. And in GRC, where the objective is not merely efficiency but trustworthy governance of the organization, that precision matters a great deal.
The market does not need less innovation. It needs more honesty about what innovation actually is.