As enterprises grapple with increasingly complex hybrid work environments, a new paradigm is emerging: Agentic AI. An operating model engineered to not just think, but to act as well.
Because this technology can autonomously predict, diagnose and resolve issues before they compromise performance, it represents a smarter way to approach the menial IT tasks that consume so much human time and attention. And in doing just that, it guides organizations closer to the prospect of zero-touch digital operations.
Agentic AI is certainly exciting – and quite possibly game-changing. That’s why this article will explore its transformative potential when it comes to managing IT operations and digital experiences (DEX) – examining how its ability to remediate on behalf of support teams can turn reactive, ticket-driven support processes into proactive, self-healing ecosystems.
From Reactive to Autonomous IT
For years, businesses have depended on helpdesks run by IT professionals trying to cope with endless queues of Level 1 and Level 2 support tickets. It was manageable when operating systems were simple (albeit time and resource-draining). Now, though, modern digital estates are too complex and fast-paced for a fully human-led approach to be viable.
While generative AI chatbots and triage tools have been introduced to streamline this process, many are fundamentally flawed because they require technicians to manually complete a fix. Despite these innovations requiring investment and expertise to deploy, IT teams are still left to sift through a list of unresolved incidents.
Agentic AI offers something different. It automatically intervenes as soon as signs of deterioration are predicted or detected. The beauty of these agents is that they’re capable of taking both proactive and reactive action when required meaning low-value requests are handled without troubling specialists.
This redelegation of tasks empowers enterprises to reduce their downtime and operating expenses. It’s a win-win situation: the automated technology removes the friction of everyday technical problems, while simultaneously building a high-quality digital service that manages its own health.
The Human-AI Collaboration Shift
As it matures, Agentic AI promises to change the relationship between employees and IT. With its help, users can collaborate with intelligent agents embedded directly into their workflow tools, rather than waiting for their colleagues in IT to resolve issues when they find the time.
This is possible because Agentic AI is designed to deliver proactive remediation on the signs of any degradation it anticipates, while continuously scanning through full-fidelity observability. Plus, when necessary, it can also fulfil traditional self-service requests initiated by employees – although in theory its predictive ability should minimize the need for that. In either case, the AI proceeds through four sequential automation steps to achieve this:
- Catch the signal: Agentic AI actively listens to the digital infrastructure to detect early signs of performance issues or alternatively receives a direct report/request from the end-user.
- Establish the issue: Analyzing telemetry supplied via an observability platform, Agentic AI identifies the user’s device, retrieves all relevant correlated analytics, and diagnoses the root cause.
- Determine the remediation approach: Checking the workflows, agents, and skills at its disposal, Agentic AI selects the best corrective measure – gathering metadata, correlating events in the data store, and communicating with Generative AI for recommendations.
- Execute the fix: Locating the original user endpoint, Agentic AI reviews all its health data and orchestrates the use of relevant agents and skills to complete the fix. Finally, it reports back to the user with a summary.
If this process doesn’t produce an effective result, Agentic AI escalates the incident to a human specialist with any relevant context attached. More often than not, this means the end-user gets a pre-emptive resolution for any issues that don’t require advanced expertise – way before any application or network degradation can affect their experience.
Establishing Governance and Trust in Agentic Systems
The question of AI’s impact on human agency is dominating conversations in all industries. After all, if Agentic AI starts to take greater autonomy over digital environments, how can IT professionals retain a sense of control, transparency, and ethical responsibility?
Crucially, IT teams can in fact maintain full authority over the AI workflow’s full automation capacities. Many organizations configure “human-in-the-loop” (HITL) steps to retain a say over what Agentic AI can access – mandating which problems it can autonomously remediate, authorizing completions, and deferring complex tasks to specialists.
Within that predefined remit, the AI automatically creates an unambiguous log of the actions it takes for auditing purposes. Each incident logged in that digital paper trail represents an input into a learning loop, which helps the AI adapt to its organization’s priorities and governance standards – refining its predictions, accelerating its workflows, and improving the accuracy of its responses.
It’s important to remember that Agentic AI isn’t here to replace the workforce. Instead, it’s designed to promote experts by taking the repetitive tasks off their plate. With that barrier lifted, specialists can refocus their efforts on extracting more value for their business – meaning “complexity finally scales without scaling the number of people giving instructions.”
A New Kind of Teammate
On a strategic level, the benefits of integrating Agentic AI are abundantly clear. These agents are essentially highly efficient, well-connected, and knowledgeable new recruits willing to perform the tasks nobody else has time for. With these new “digital teammates” on board, businesses can extract a far higher quality of digital experiences from the resources they expend.
Essentially, Agentic AI’s ability to invisibly execute a resolution and then inform the end-user inverts the traditional IT support dynamic; it’s all about the agent actioning and then reporting a successful intervention, instead of the human needing to ask for a fix. As a result, Mean Time to Detect becomes a far more decisive performance metric than Mean Time to Resolution.
Thanks to this fresh framework for productivity and resilience, the IT specialists that were previously inundated by low priority tickets are saved from burnout. They’re given the opportunity to rebalance their workload, reinvigorating their sense of importance as their roles evolve towards technical innovation and value creation.
The Future of AI is Agentic
AI is rewriting the rules of IT operations at a speed never seen before. Nowhere is that more evident than in the integration of self-service agents, which are rapidly becoming the new modus operandi for human-machine collaboration within digital ecosystems.
These autonomous systems can now resolve problems before anyone’s even aware they exist – building a zero-touch digital architecture with a narrowed gap between detection and response. With that, businesses are able to perform more consistently, adapt more quickly, and unlock the full potential of their technology and their teams.
Above all, Agentic AI gives the gift of precious time to IT teams – empowering them to step away from endlessly repairing systems of the past, so they can reinvest their expertise into pursuing the future of digital transformation instead.
