How Agentic AI Is Redefining IT Operations: 8 Transformative Use Cases

Artificial intelligence is moving from automation to autonomy — and the next major leap in that evolution is Agentic AI. Unlike traditional AI models that only respond to instructions, agentic systems can reason, plan, and act on their own to accomplish complex tasks with minimal human supervision.

In IT operations, this means a fundamental shift from monitoring and reacting to self-optimizing, self-healing systems that keep enterprises running seamlessly. As Yogesh Joshi, Senior Vice President of Global Product Platforms at TransUnion, explains:

“Availability, reliability, scalability, and performance at the lowest cost are critical success measures for IT operations. Traditionally, we’ve relied on people, processes, and technology to ensure these measures — Agentic AI changes everything.”

Let’s explore eight transformative use cases showing how Agentic AI is redefining IT operations today.

1. Smarter compute resource utilization

Agentic AI continuously monitors compute resources and dynamically adjusts workloads to optimize performance and cost. It can automatically select instance types, adjust configurations, and manage scaling in real time — ensuring that infrastructure aligns perfectly with workload demand.

Beyond performance tuning, these agents can detect anomalies in data flow and autonomously apply remedial actions, keeping operations stable and secure.

2. Automated IT support and maintenance

Matt Lyteson, CIO of Technology at IBM, predicts that Agentic AI will transform IT support by shifting focus from manual automation to autonomous decision-making. Agents can maintain servers, patch vulnerabilities, perform root-cause analysis, and even recommend environment optimizations — all without direct human intervention.

By combining AI orchestration, automation, and data access, IT teams can build and deploy support agents that reduce cycle times and increase uptime across large-scale systems.

3. Faster problem resolution

Agentic AI closes the gap between detecting an issue and resolving it. Loren Absher, AI Advisory Director at ISG, notes that these systems can monitor service-level objectives (SLOs), correlate logs, propose fixes, and execute rollbacks autonomously.

The result? Mean time to resolution (MTTR) can drop from hours to minutes. Human teams can then focus on setting guardrails, designing safe rollback paths, and optimizing change velocity — instead of firefighting every incident.

4. Improved customer support experience

According to Rowena Yeo, CTO at Johnson & Johnson, Agentic AI is transforming customer service by allowing multiple agents to coordinate across systems and resolve complex inquiries faster.

When several AI agents collaborate — one handling customer queries, another pulling data from backend systems, and a third validating workflows — the result is faster, more consistent service and happier customers. This also improves organizational agility, as agents continuously learn and adapt from prior interactions.

5. Real-time infrastructure decision-making

Mike Anderson, Chief Digital and Information Officer at Netskope, explains that Agentic AI allows IT to move from reactive to proactive operations. An agent can detect early signs of database failure, correlate signals from monitoring tools, and decide whether to restart a service or reroute requests — all in real time.

This intelligence prevents small problems from escalating into outages and enables IT engineers to focus on resilience and architecture improvements rather than manual fixes.

6. Autonomous software testing

David Colwell, VP of AI at Tricentis, highlights that Agentic AI is accelerating software testing and quality assurance. AI agents can automatically generate, execute, and validate test cases, ensuring higher coverage and reduced manual effort.

By delegating repetitive testing tasks to agents, engineering teams can concentrate on integration challenges and exploratory testing — improving both speed and quality. However, Colwell warns that strong governance and oversight are crucial to prevent AI from introducing new errors through over-automation.

7. Boosting team productivity

Agentic AI allows IT teams to offload routine maintenance and monitoring tasks, freeing up time for creative problem-solving and innovation. Dhaval Jadav, CEO of Alliant, observes:

“While simpler automation tools execute repeatable work, agentic AI can perform complex, contextual tasks fluently — changing the nature of IT work itself.”

By starting small — with localized pilots that demonstrate measurable value — organizations can gradually build confidence, improve efficiency, and scale AI adoption across departments.

8. Enabling self-healing systems

Ryan Achterberg, CTO at Resultant, envisions the self-healing enterprise as the future of IT operations.

“Imagine an AI that spots a memory leak, spins up a replacement server, redirects workloads, and patches the faulty node before anyone even knows there’s an issue,” he says.

Agentic AI makes this vision possible by combining observability, predictive maintenance, and autonomous remediation. The result: minimal downtime, continuous service stability, and fewer sleepless nights for engineers.

The road ahead

Agentic AI represents an architectural shift in IT operations — from human-managed automation to AI-managed intelligence. It’s not about replacing engineers but augmenting them with intelligent systems that can act, learn, and adapt autonomously.

As enterprises adopt agentic frameworks, IT will evolve into a proactive, self-optimizing ecosystem where humans focus on innovation, design, and governance — while AI ensures continuous performance, reliability, and security.

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