8 ways agentic AI will transform IT operations

Capable of accomplishing specific tasks with virtually no human supervision, agentic AI promises to revolutionize a wide range of IT operations and services. This powerful new approach uses AI agents — models that mimic human decision-making — to address and resolve problems in real-time.

IT operations cut across multiple domains, including networking, storage, computing, and security, that provide the foundation for business platforms and product portfolios, says Yogesh Joshi, senior vice president of global product platforms at credit reporting firm TransUnion. “Availability, reliability, scalability, and performance at the lowest cost are critical success measures for IT operations,” he explains. “Traditionally, we’ve relied on a combination of people, processes, and technology to ensure these success measures.” Agentic AI changes everything.

How can agentic AI help your organization work faster and more productively? Here are eight ways this powerful new technology can be used to speed operations and cut costs.

1. Improved compute resource utilization

AI agents can assess compute resource utilization in real-time, selecting optimal instance types, configurations, and scaling parameters to dynamically tailor infrastructure to workload demands, Joshi says.

“AI agents can also continuously monitor data ingress, access, usage, and egress, applying policy-based principles to detect anomalies and autonomously initiate remedial actions,” he adds

2. Automated support

Agentic AI is kicking off an era in which IT pros will think less about automating repeatable tasks and focus on crafting the most helpful possible support agents, says Matt Lyteson, CIO of technology at IBM. “This will involve teaching the agents themselves, but also ensuring speed, scale, and security throughout the entire process.”

“When done properly, agent-driven transformation has the potential to drastically streamline the way the way we run everything in IT,” Lyteson says. He points to site reliability engineering as a basic example. “An agent can autonomously ensure that servers are maintained and kept up-to-date, perform root cause analysis, adjust large swaths of IT operational data, and make recommendations on how to optimize the environment.”

Traditional IT automation can already do much of this, but agentic AI takes that to a new level of speed, resulting in drastically reduced cycle times.

To unlock maximum value from agentic AI, it’s necessary to build on an enterprise AI platform that offers orchestration, data access, and automation capabilities, in addition to AI. “These elements will let you experiment, build, and deploy testing agents quickly while staying true to software lifecycle management and core IT operations such as observability, service management, and information security,” Lyteson says.

3. Faster problem resolution

Agentic AI marks the difference between having a ticket automatically created and having an issue automatically resolved, says Loren Absher, AI advisory director at technology research and advisory firm ISG. “An agent can monitor service-level objectives, correlate logs and metrics, propose a fix, run a canary, execute inside a change window, and then auto-roll back if the SLO dips.”

Absher says adopters can expect mean time to resolution (MTTR) to drop from hours to minutes or even seconds on common incidents, with fewer handoffs and cleaner postmortems because every agent action is logged.

“The human work shifts from clicking through runbooks to designing safeguards, including pre-approved plays, blast-radius tags, and rollback paths,” he says, resulting in steadier change velocity and fewer heroics.

4. Improved customer support

One of the areas where we’re seeing early examples of agentic AI providing value is in customer service, says Rowena Yeo, Johnson & Johnson’s CTO. “By bringing together multiple agents to coordinate across systems, companies can resolve complex inquiries more efficiently,” she explains. “This not only helps teams work faster but also increases customer satisfaction.”

Agentic AI’s ability to execute complex, contained tasks offers an opportunity for experimentation and operational optimization, Yeo says. “These capabilities increase our agility and allow us to be more responsive and efficient.”

5. Rapid decision-making on infrastructure issues

Instead of following a fixed script, an agent can analyze a situation, decide which action is most appropriate, and then recommend the approach to its operator, says Mike Anderson, chief digital and information officer at Netskope, a firm specializing in real-time network security and management services.

For example, in a production support scenario, the AI agent might detect a database service that’s becoming unresponsive, correlate all related signals, and then recommend restarting the service rather than rebooting the entire server.

AI agents will make IT more proactive and less reactive, Anderson says. “Team members will experience fewer disruptions because AI can prevent small problems from becoming big ones,” he explains.

In production support, it could recommend a remediative action to address a degrading application before it causes an outage, with an engineer approving the action. “IT teams then spend less time firefighting and more time on strategic improvements that enhance resilience, efficiency, and user satisfaction.”

6. Streamlined software testing

Agentic AI is transforming software testing, says David Colwell, vice president of artificial intelligence and machine learning at Tricentis, an automated software testing and quality engineering services firm. “It’s advancing everything from test case generation to test automation,” he states.

“Teams can now largely shift baseline testing to AI agents to ensure high-impact areas are thoroughly covered,” Colwell says, adding that technology will allow human engineers to focus on more complex challenges such as addressing intricate integrations and exploring more edge cases. “In turn, this helps teams to accelerate delivery and improve long-term system resilience.”

Colwell is already starting to see AI agents influence software speed, quality, and risk reduction. “With AI agents managing more repetitive or baseline tasks, IT teams are empowered to resolve more issues, faster.”

Colwell also warns CIOs that, without proper governance, the same agents that accelerate workflows can also create new risks. “We have seen agents spiral into loops where they rewrite tests incorrectly or even erroneously delete entire sections of code,” he says. “AI agents have the ability to transform IT operations for the better, but only when paired with strong guardrails and human oversight.”

7. Enhanced team productivity

With agentic AI, IT leaders can free up large quantities of their teams’ time, enabling staff members to direct their efforts to more creative endeavors and projects, thereby enhancing overall innovation, says Dhaval Jadav, CEO of business consulting firm Alliant.

“Current agentic AI is the first iteration with inherent autonomy,” Jadav observes. “While simpler versions, like AI workflows and automation, can conduct basic, repeatable work at a consistent pace, agentic AI can do complex work more fluently.”

Jadav believes agentic AI will grow powerful enough to change the work that most team members do. “These impacts will be felt quickly and deeply,” Jadav predicts.

Yet Jadav warns that agentic AI isn’t necessarily the slam dunk solution it may appear to be. “Getting it to work as intended may be difficult, expensive, and time-consuming relative to its upside,” he cautions. “This opens up a broader conversation about modernizing and streamlining your organization.”

As a result, Jadav advises IT leaders to start small and build their way up. Begin with small, localized projects that demonstrate a proof of value, while helping to get IT team members comfortable with agentic AI and its potential, he says. “All of this will help contribute to a strong foundation, which is both the first and most important step.”

8. Self-healing systems

Agentic AI is emerging as the backbone of a self-healing enterprise, in which systems aren’t just monitored but self-managed, says Ryan Achterberg, CTO at technology and business consulting firm Resultant.

“Picture 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. “No scrambling, no outages, no sleepless nights.”

The nature of IT operations changes significantly when it becomes agentic, Achterberg adds. “Service interruptions are no longer sudden crises requiring urgent response.” Instead, detection and recovery processes occur rapidly, resulting in consistently stable service levels.

The very concept of work also changes with agentic AI. “The endless tier-one grind fades into the background while repetitive issues resolve automatically,” Achterberg says. Engineers are then free to focus on tasks such as platform design, model tuning, and crafting guardrails. “They spend less time asking what broke and more time shaping how the system should behave.”