Automation as Anesthesia
By Marzena Burakowska | April 10, 2025
There’s a new term getting serious attention in IT and digital transformation circles: Agentic AI. It sounds futuristic—and maybe even a little buzzwordy—but it’s gaining traction fast. So, what is it, really? Is it the next wave of automation, or just another overhyped promise?
Let’s take a closer look at what agentic AI is, how it differs from what we’ve seen before, and why it might be positioned as a game-changer for IT operations, AIOps, and broader digital strategy. But beyond the promise, it’s just as important to examine the practical challenges of handing more decision-making power to machines—and whether today’s enterprise IT environments are ready for that shift.
From Automation to Autonomy
Agentic AI refers to systems that don’t just respond to inputs or follow pre-programmed rules—they can interpret high-level goals, make decisions, and execute complex tasks independently. In theory, they act with purpose. Unlike traditional automation, which requires detailed step-by-step instructions, agentic AI can evaluate context, weigh options, and carry out a sequence of actions with minimal human involvement.
For example, instead of alerting a team when a server’s CPU spikes, an agentic AI system might investigate the root cause, weigh possible solutions, and apply a fix—all on its own. That’s a significant leap from rule-based automation or current-gen AIOps tools that still depend on human intervention for decision-making and execution.
Why It Matters for IT Operations and AIOps
In modern IT environments, where complexity and scale continue to grow, the potential appeal of agentic AI is obvious. It promises faster resolution times, smarter use of resources, and fewer late-night alerts. Within AIOps, the model could shift from reactive alerts and dashboards to proactive, autonomous action.
Theoretically, this lets IT teams move away from firefighting and toward higher-value initiatives like architectural planning, security hardening, and innovation. Agentic AI could also help integrate workflows across fragmented tools and departments, enabling a more holistic and automated IT landscape.
But “could” is doing a lot of work here.
What It Means for IT Teams
If agentic AI lives up to its vision, it will certainly reshape IT roles. Engineers may spend less time writing scripts or manually responding to alerts and more time configuring agents, auditing decisions, and refining how these systems operate. IT teams would shift from execution to oversight—effectively becoming AI orchestrators rather than task executors.
This isn’t necessarily about eliminating jobs; it’s about changing what those jobs look like. Human judgment will still be critical, especially in edge cases, ethical decision-making, or when systems behave in unexpected ways. The challenge will be ensuring teams remain engaged and skilled, even as machines handle more of the routine work.
Risks and Challenges to Consider
With greater autonomy comes greater complexity—and risk. One of the biggest concerns is that an AI agent might take an action that seems logical from its perspective but is fundamentally wrong. A misdiagnosis, a misunderstood pattern, or poor training data could result in harmful decisions being executed automatically and at scale.
There’s also the issue of transparency. Can a system explain why a change is made? And who’s accountable when things go wrong? In highly regulated industries or mission-critical environments, these are more than philosophical questions—they’re operational and legal concerns.
Security is another major issue. An agentic AI with access across systems becomes a high-value target. If compromised, it could be misused to execute malicious actions far more efficiently than any individual attacker. Guardrails, governance, and robust access controls aren’t optional—they’re essential.
Then there’s the cultural challenge. Many IT teams already struggle to adopt traditional automation or cloud-native practices. Introducing autonomous agents into that mix—especially in environments with legacy systems or fragmented architectures—won’t be easy. Resistance, uncertainty, and trust issues are likely. And for organizations with technical debt and brittle infrastructure, the promise of agentic AI might feel miles away from what’s achievable.
Final Thoughts
Agentic AI is more than a trendy phrase—it represents a serious and intriguing direction for enterprise tech. The idea of intelligent, self-directed agents supporting or even running parts of IT operations is compelling and, for some, even inevitable. It has the potential to reduce complexity, accelerate response times, and free teams from the grind of repetitive tasks.
But let’s not get ahead of ourselves. As usually in IT, building reliable, secure, and trustworthy agentic systems is complex— especially in environments still dealing with siloed data, outdated tools, and inconsistent processes. Most organizations are nowhere near ready to plug in autonomous agents and watch the magic happen. The infrastructure, culture, and governance needed to make it work are often missing—or underdeveloped.
So yes, agentic AI may one day change the way IT works. But for now, it remains more of a promise than a proven solution. That doesn’t mean it should be ignored—but it does mean we should approach it with curiosity and caution. Start small. Pilot carefully based on real use cases to make sure that value will be realized.
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