Agentic AI Hype vs Reality: What to Expect Next
- Mandy Lee
- 3 days ago
- 6 min read
Agentic AI is having its “big moment.” Every vendor deck has agents. Every roadmap has autonomy. And every leadership team is being asked some version of: “Should we be doing this too?”
The problem is not that agentic AI is fake.
The problem is that the hype has sprinted ahead of the operational reality.
Gartner’s tone on this is unusually direct: it predicts over 40% of agentic AI projects will be canceled by the end of 2027, driven by escalating costs, unclear business value, or inadequate risk controls. That is not “agents are dead.” That is “agents are expensive, messy, and easy to misapply.”
So let’s ground this. What is agentic AI actually? What are analysts expecting? And what should businesses do if they want outcomes instead of a graveyard of pilots?
What is agentic AI?
Agentic AI is software that can plan, decide, and act toward a goal, often by calling tools, triggering workflows, and coordinating multiple steps, with minimal human intervention.
Forrester’s framing is clean: traditional AI and GenAI systems (even with RAG) are great at answering and summarizing, but agentic systems can “plan, decide, and act autonomously,” orchestrating workflows.
Gartner makes a similar distinction in its customer service prediction: earlier AI helped with text generation and summaries, but agentic AI introduces systems that act autonomously to complete tasks, using agents and bots to automate interactions.
The important nuance: “agentic” is not a synonym for “chat UI” or “LLM in a loop.” It implies execution, not just conversation.
The market signal: everyone wants autonomy, but nobody wants surprises
The promise is obvious:
Faster throughput (more work completed per human)
Lower operational cost in repeatable workflows
More self-service and fewer escalations, especially in support
Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues and drive a 30% reduction in operational costs.
That is the upside case. And it is real enough that it is pulling budgets forward.
But the downside case is also real: systems that take actions can take the wrong actions. They can misinterpret goals, drift over time, or behave unpredictably when the environment changes.
Forrester explicitly warns that these systems can be misaligned, producing actions that are undesirable or harmful, and that companies need to test, learn, and iterate because we are early in the impact curve.
Gartner’s vendor guidance is blunt: autonomy without oversight breaks trust. Enterprises will not tolerate black-box behavior, and in regulated industries, explainability and auditability are nonnegotiable.
So the real market signal is not “agents everywhere.” It is: controlled autonomy wins.
Why the hype is so loud right now
Two reasons are driving the noise.
1) The definition is being stretched beyond usefulness
Gartner calls out “agent washing” directly: vendors are rebranding existing products (assistants, RPA, chatbots) as agentic without substantial capabilities, and Gartner estimates only ~130 of the “thousands” of agentic AI vendors are real.
When the term means everything, it means nothing.
2) Pilots are easy. Production is hard.
SS&C Blue Prism describes the shift from “promise” to “proof,” in which 2026 becomes the year businesses ask, “Is it working?”
That is the inflection point most teams are about to hit.
And it matches Gartner’s cancellation prediction: many current efforts are early-stage experiments driven by hype, misapplied use cases, and an underestimation of the cost and complexity of deploying agents at scale.
What analysts actually expect (not the marketing version)
Here are the expectations worth taking seriously, based on the sources above.
Expectation 1: Agentic AI will show up inside mainstream software, not just “agent products”
Gartner predicts 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024. So the question is less “will you adopt agents?” and more “where will agent behavior appear in tools you already use?”
Expectation 2: Autonomy will expand, but the winning pattern will be bounded autonomy
Gartner’s guidance to vendors is basically a checklist for survival:
design for transparency and trust
log every decision
track reliability metrics like failure rates, drift, reproducibility
limit autonomy to controlled environments with constraints and fail-safes
validate use cases before scaling
model total cost early (governance, observability, integration)
That is not a vibe. That is engineering.
Expectation 3: ROI will be the forcing function
Blue Prism calls this the “ROI Awakening”: leaders will double down on pilot-to-production workflows, measurable use cases, and outcomes like processing time, quality, and cost.
Translation: if you cannot explain the value in numbers and show how you control risk, the budget moves on.
Expectation 4: Many projects will be canceled because they are trying to automate ambiguity
Gartner’s cancellation reasons are telling: cost, unclear value, inadequate risk controls.
That typically shows up when teams try to make agents “general” too early, or when they skip the hard work of making rules explicit.
Or, bluntly: they tried to automate something that humans themselves have not agreed on.
The part most teams underestimate: ownership of logic
Agentic AI forces a question that traditional software could sometimes avoid: Who owns the logic?
If an agent is making decisions and taking actions, “requirements” stop being a ticket and start being a liability. The logic has to be shared, inspectable, and correct.
This is where many teams hit the wall: the logic lives in fragments (tickets, docs, chats, assumptions), and what ships is often “the logic that survived the translation process,” not the logic everyone intended.
AI does not fix that. It can accelerate the build, but it can also accelerate the path to implementing the wrong thing.
If you want agents in production, you need a way to make decision logic visible, testable, and reviewable across roles, before it becomes automated behavior.
A practical way to think about agentic readiness
If you are evaluating agentic AI for your business, here is a simple maturity path that aligns with the analyst warnings.
1) Start with “bounded actions,” not “general autonomy”
Pick workflows where:
the goal is measurable
the action space is limited
failure modes are understood
escalation is possible
This aligns with Gartner’s “validate use cases before scaling autonomy” guidance.
2) Treat observability as a feature, not an internal tool
If an agent acts, you need:
decision logs
traceability
drift monitoring
clear traceability from intent → decision → action
3) Model total cost honestly
Agentic AI raises demands across UX, security, and integration, and can introduce technical debt if not planned. If your business case does not include governance and ongoing monitoring, it is not a business case.
4) Design for “human + agent” collaboration, not replacement
Blue Prism’s point is practical: prioritize agents that support human workflows, where people lead, and AI assists.
In customer service, Gartner also warns that teams will need to support both human customers and “machine customers,” and set interaction policies around privacy, security, and escalation.
5) Decide how your organization will validate logic
This is the hard part, and it’s where most projects stall. Gartner says many projects are hype-driven POCs that stall before production because organizations underestimate cost and complexity at scale.
A big chunk of that complexity is validating what the agent is allowed to do and proving it will do so reliably. If your “verification workflow” is basically “we’ll test it,” you are already behind.
Where we are going (a realistic forecast)
Here is the trajectory the sources collectively point to:
More agent behavior inside products you already buy (enterprise software embedding agents).
Aggressive adoption in domains with clear ROI (support is the headline example).
A shakeout where poorly governed or poorly scoped projects get canceled (the 40% figure is the warning shot).
A trust and control arms race: vendors that can prove transparency, auditability, and constrained autonomy will win procurement.
A re-centering on shared understanding: the highest leverage work will be making intent and logic explicit enough to automate safely.
This is also why “agentic AI” will stop being a category and start being a capability. The differentiator will not be who has agents. It will be who can operate them without surprises.
The bottom line
Agentic AI is not a magic wand. It’s a new class of systems that can take actions, which makes it both powerful and risky.
The hype is loud because the upside is real. The cancellations will be real too, because production-grade autonomy demands more than demos. It demands measurable ROI, disciplined scoping, governance, and logic you can actually inspect.
So if you want to be on the right side of this wave, focus less on “how autonomous can we be?” and more on this:
How do we make decisions visible, verifiable, and safe before we let software act on them?
That’s the gap Leapter is designed to close. Leapter adds a deterministic logic layer around agent behavior, so teams can define and review decision logic in a human-readable way, with traceability from intent to rules to outcomes. Domain experts and engineers can align on what the agent is allowed to do, audit changes over time, and ship with clearer controls than “trust the model.”
If you’re building enterprise agents and need governance you can prove, explore how Leapter helps teams turn autonomy into something you can operate.