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Walk into any boardroom today and you’ll hear the same buzzwords circulating: AI, Gen AI, LLMs, and now, increasingly, “agentic AI.” Like many tech terms, it’s gaining traction faster than it’s being understood—and that’s a problem. Why? Agentic AI doesn’t just represent the next wave of artificial intelligence; it changes how work gets done, who—or what—does it, and ultimately, who’s in control.
Let’s cut through the hype. What is agentic AI, and why should enterprise leaders be paying close attention?
From Prompts to Purpose: What Makes Agentic AI Different?
Agentic AI refers to systems that don’t merely respond to inputs—they take initiative. These agents can be assigned goals, which they break into subtasks and execute independently. They operate in loops, checking outcomes, adjusting strategies and iterating until the objective is met. They don’t wait for the next command; they determine what to do next.
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In short, traditional AI identifies patterns. Generative AI creates content. Agentic AI makes decisions. That shift, from output to outcome, from reaction to proactive action, is what makes this technology transformative—and risky.
Agentic systems are already appearing across enterprise workloads. Some are handling customer service support, coordinating support tickets, optimizing cloud resource usage, or automatically analyzing logs and deploying code. Their appeal is clear: increased efficiency, automation at scale and more agile digital operations. But, that autonomy comes with a steep learning curve—and an entirely new set of infrastructure and governance requirements.
These Aren’t Just Smarter Chatbots
Unlike earlier generations of enterprise AI, agentic systems are not deterministic. They don’t simply execute predefined logic or select the most probable next word. They actively decide how to pursue goals, using APIs, tools and sometimes other agents. That means, they can surprise you.
Consider a cloud optimization agent tasked with reducing computing costs. The agent might succeed by suspending idle environments—or it might suspend critical systems at 3 a.m. without notifying the Site Reliability Engineering (SRE) team. A meeting-scheduling agent might rearrange calendars to boost team productivity—or inadvertently cancel the only cross-functional sync meant to resolve a major issue. These examples are not theoretical. The autonomy of these systems is the point. The trade-off for such advanced capabilities and increased productivity is that the stakes are higher, the margin for error is smaller, and the need for oversight is greater than ever.
The Risks of Autonomy
In this rush to deploy, it’s easy to mistake speed for strategy. Every CIO is under pressure to deliver more automation, more savings, more innovation—yesterday. But, rushing into agentic AI without preparation is a recipe for disappointment—or worse, disaster. Businesses need more than a model. They need governance, transparency and guardrails.
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Agentic AI introduces a distinct class of risks tied to its autonomy. These systems can act without human review, operate faster than teams can monitor and pursue poorly defined goals in unintended ways. Their ability to integrate with tools and systems heightens the stakes, and missteps could expose sensitive data, trigger changes in critical environments or cascade actions across interconnected platforms.
By the time something goes wrong, the damage may already be done.
To reduce risk, enterprises should implement:
Clearly scoped rules, permissions and access boundaries
Audit logs for every agent action
Sandboxed execution environments
API rate limiting and strict access control
Agentic AI Is Changing Human Roles
Agentic AI is also rewriting workforce expectations. These systems aren’t meant to replace employees; they’re designed to automate the repetitive, high-frequency, rules-based work that often bottlenecks teams. This shift requires humans to change roles from executors to supervisors, from ticket-pushers to exception handlers and strategic designers, and the list goes on.
Organizations must begin training teams on how to:
Define agent goals and constraints effectively
Interpret agent behavior using logs, summaries and telemetry
Intervene safely when something goes off track
The emergence of agentic AI mirrors previous waves of automation. The difference is that this time, the “system” can rewrite its own task list in pursuit of an objective. That requires an entirely new human-machine partnership model.
Final Thought: Autonomy at Hyperscale Requires Intentional Design
The temptation, of course, is to move quickly. The pace of innovation is relentless, and AI pilots are becoming table stakes. But, speed without design leads to fragile implementations, and in agentic systems, the risks compound quickly.
The companies that will succeed in the agentic era aren’t the ones that plug in the most agents the fastest. The ones who understand what agency means and build systems that align with human goals will rise to the top.
We’ve reached a new inflection point in enterprise AI. The conversation can no longer just be about capability; it has to be about control. Because, when you give software the power to act, you also give it the power to make mistakes. The question every technology leader should be asking now is simple: Are we ready for that?
Agentic AI is not just another productivity hack. It’s a rethinking of how digital work is defined and delegated. It’s early, but it’s real, and the time to get smart about it is now.
About the Author
Rob Mason, CTO, Applause, has more than 30 years of operational, management, and software development experience across various companies, languages, platforms and technologies. A meticulous builder and obsessive tester, Rob’s teams produce innovative, robust software. Prior to Applause, Rob was founder and CTO at Nasuni, now a Vista portfolio company, where he took the company from initial product concept to more than 10 petabytes of managed storage consisting of billions of files in a global file system. Prior to Nasuni, Rob oversaw all development and quality assurance as the VP of engineering at Archivas, acquired by Hitachi. Rob holds over 40 patents in his name including several in the area of test automation. He has a Bachelor of Science degree from Rensselaer Polytechnic Institute and an MBA degree with honors from Rutgers University.