Many of us work in the information technology sector and have witnessed remarkable transformations in the tech landscape for over a decade. In the modern era, complex tasks and operations are increasingly managed through a programmatic approach. A revolutionary breakthrough in this domain is the rise of AI. Autonomous systems now possess the ability to perceive, interpret, and respond to dynamic inputs, powered by sophisticated large language models known as Agentic AI.

Before we begin, most of us are new to the AI try to understand some common terminologies used. Agentic AI, is an autonomous system that leverages LLM’s capabilities to perceive, interpret and act on dynamic inputs. There are traditional AI also exists, those relies on predefined rules and static datasets, but Agentic AI continuously learns from user interaction, behavioural patterns, real-time data, fine-tune its decision making over the period. Some of the demonstrative examples of Agentic AI’s are Healthcare AI, Smart Assistants, autonomous mining vehicles, AI-powered robots etc.

On the other hand, we often hear Generative AI, this is a subset of AI focused on creating new content or data. It generates relevant and original text, image, music and even videos. It uses it knowledge gathered from data and learns structures from it, means utilizes vast amounts of training data to generate outputs based on patterns they have learnt. Some of the illustrative cases of Gen AIs are OpenAI’s ChatGPT, DALL-E, Stable diffusion, Runway ML etc.

Below table gives us the small comparison between GenAI, Agentic AI and Traditional AI solutions and its features.

Descriptions	Traditional AI	Generative AI	Agentic AI
Core Function	Automates repetitive and rule-based tasks	Produces content (text, code, images, etc.)	Executes goal-oriented actions and decisions
Autonomy Level	Low – Relies on predefined rules and algorithms	Variable – Requires user prompts or guidance	High – Operates independently with minimal administration
Learning Approach	Depends on specific rules and human intervention	Data-driven learning – Analyses and adapts based on existing data	Boosted learning – Continuously improves through experience

The Key Characteristics of Agentic AI.

Agentic AI functions autonomously, continuously learning and adapting to changing conditions. It makes independent decisions and takes action without ongoing human oversight. These systems analyse their environment, reason through complex scenarios, and refine their strategies in real time to efficiently achieve their objectives.

The Agentic AI formulates specific objective and execution begin toward completion, it creates sub-tasks and action sequences. Autonomous system does not mean it will not require human intervention; it will have human intervention/interaction not on every step to accomplish an objective. This autonomy depends on how system is configured, sometimes if there are critical steps that could requires human intervention or approval it looks for confirmation else specified goal can be achieved without virtual supervision.

Agentic AI interacts with its environment—whether physical or digital—by taking actions that shape outcomes. In essence, it responds to changes in real time, adapting dynamically to function effectively in complex and unpredictable situations. an AI-powered financial trading system analyses market fluctuations and modifies its strategies to optimize investments, adapting to trends and unexpected shifts without human supervision. It helps traders to achieve the best possible results by reacting to market trends.

Adaptiveness in the system is learned from experience, refining its responses based on feedback. The more input it receives, the more effectively it adjusts to new situations and environments. For example, AI-driven personal assistant learns from user preferences such as shopping habits, daily grocery shopping, scheduling the meeting etc.

The AI system functions across extended periods, executing multiple steps to accomplish objectives. The agent can combine state and context over time, helping user achieve long-term objectives with minimal human involvement. The agents become proactive rather than reactive for example autonomous monitoring system, based on the user behaviour anomaly detection or condition of the DataCenter facilities, or incident predictions etc.

Leveraging Agentic AI for IT operations

The Agentic AI systems are independent system that act autonomously with goals, planning and some of the decision-making qualifications. Agentic AI surpasses conventional chatbots and generative AI by delivering dynamic, context-aware interactions, continuously learning from experience, and providing highly personalized responses.

Agentic AI at Helpdesk

Traditional helpdesk and ticketing systems are slow and outdated, there are many manual steps involved such as ticket triage, addressing to some of the repetitive tasks, offering customers and agents quick access to information, these activities are time-consuming and inconsistent ticket handling and knowledge gaps can impact on the customer satisfaction, delays in resolving issues can frustrate end users, while prolonged downtimes negatively impact overall company productivity or jeopardize the entire operations.

Modernizing the helpdesk operations through integration of Agentic AI can help organization to elevate the overall efficiency and performance. Agentic AI improves helpdesk operations by delivering quicker, more tailored, and highly efficient support through automation, proactive problem detection and real-time assistance. Below are the few benefits:

• Proactive issue detection and resolutions
• Improved FCR and reduced MTTR
• Improved agent efficiency and productivity
• Improved customer experience
• Data-driven insights for knowledge base articles
• Cost-optimization

Some of the use cases where agentic ai can elevates the helpdesk operations such as;

Ticket creation and categorization through AI reduces response times and ensuring that critical issues are addressed promptly. This will improve workflow efficiency and streamlines helpdesk operations.

Automated troubleshooting and providing the resolutions through AI, the diagnostics system would identify and resolve issue without helpdesk service technicians. This will reduce the downtime and ensures a smoother user experience.

Knowledge base management through agentic ai would continuously updates and refines helpdesk documentation, end-user receive updated information for troubleshooting and support. This will improve self-service efficiency and reduces the workload on support teams.

Employee on-boarding through agentic ai would streamlines the onboarding process by providing the assistance to the new hires through IT systems, ensuring a smooth transition into their roles and responsibilities. The ai would reduces the burden on the IT support team and new joiner can be productive from day one.

There are many example the agentic ai can help to improve overall operations such as AI-driven CSAT survey, compliance monitoring, helpdesk staffing optimization, personalized user assistance, remote assistance, automated follow-ups and escalations, real time analytics and reporting, multilingual support and translations and many more.

Agentic AI deployments are most productive when these systems are integrated deeply into existing workflows. Modernization of existing system, allowing data access across an organization, these agents make more effective decision and provide robust operations based on any dynamic conditions. At the end improves the overall efficiency and improves customer satisfaction score.

Agentic AI as Field Technician

Agentic AI can play a vital role in the field support operations. Multi-agent system, equipped with autonomous planning, decision-making, and action-taking capabilities, are increasingly recognized as a powerful solution for addressing complex field service challenges, such as workflow management, capacity planning and on-site troubleshooting etc. Through Agentic AI not only modernizing the field operations but also creating smarter, faster and more resilient operations.

Capacity planning and workflow management through agentic ai would help to optimize and streamline the field service operations. Many times, in the field services dispatchers and staff schedulers struggle to create schedules that seamlessly align available resources with real-world conditions, leading to inefficiencies and service disruptions.

Agentic AI streamlines incident ticket management, automating classification, routing, and team assignment based on technician availability and skill set. A human dispatcher verifies the AI-driven decisions [sometimes it can be without supervision], ensuring efficient scheduling and validated item rolls. Furthermore, Agentic AI can interact with field service technicians in a conversational way rather than going through portal and logging into the system. Technician can interact with AI applications giving instruction to the scheduled job, equipment they need to carry out, locations and route map. This would really help and give engaging experience to technicians and avoid the frustration and time sink of typing errors.

Agentic ai can also become on-site assistant, basically when technician at site he/she could looks for company’s internal pages for troubleshooting, if agent can interact with ai system or application and give appropriate guidance [providing video, images and text] then technician can easily perform his/her task with the guidance. This type of scenario could help when you have new hire and less experienced technician on the field.

Agentic AI is set to revolutionize field operations, shifting them from a cost burden to a strategic asset. Service providers can proactively embrace this shift, moving beyond gradual enhancements toward a dynamic, intelligent service model.

Agentic AI in Managing M365 applications

The Agentic AI significantly elevates the L2 and L3 operations or managed services for Microsoft 365 that includes Exchange, SharePoint, teams and other applications. It empowers the support teams by automating complex tasks, providing real-time support and adapting to dynamic environments, results in improved productivity and decision-making. Agentic AI understands context, identifies objectives and takes the initiative to fulfil them, coordinates across multiple data sources, office 365 applications and user environments.

The Microsoft 365 applications majorly constitute of Exchange, OneDrive, SharePoint, teams. The integration of ai solutions would help in providing application support. For example, if the end-user encounters Team call connectivity issues, agentic ai solution helps to diagnose issues at network, firewall, stability of application and suggest real-time fixes or escalates the issues. Similarly, permission issues related to SharePoint, AI can verify access controls and auto-adjust settings based on predefined rules, this in turn reduces the human interaction. These are routine support tasks can handled efficiently and improves the end-user productivity. This is a non-quantifiable benefit that any organization can reap by AI adoption.

Another key use cases is infrastructure optimization, where AI can help to enhance scalability, efficiency and performance. Excessive storage and outdated SharePoint configuration leads to slow SharePoint performance. The agentic ai can understands and analyse the usage patterns and optimize storage allocations and automate cleanup tasks. Similarly, in AI can detect inefficiencies in email routing, adjust mail flow rules and ensure better bandwidth utilization. Agentic ai not only enhances reliability also minimizes operational bottlenecks.

Another example would like to iterate i.e. meeting SLA through Agentic AI.  The service-level agreement plays a vital role in any managed services operations. Agentic AI can track incoming support request classify them based on the urgency and if the incidents are not resolved beyond SLA limits, AI can automatically escalate to higher level support team, ensuring timely intervention. AI analyses historical performance data to refine resolution strategies, reducing ticket backlog and maintaining SLA compliance.

There many ways the agentic ai can help office 365 application support team in day-to-day operations. Such as predictive maintenance, self-healing systems, automated patch and update management and many more. The Agentic Ai are intelligent agents capable of handling routine, repetitive and data-heavy activities, they work alongside of technician acts as digital labor or teammates that empowers Office 365 SMEs to be more strategic, creative and fulfilled.

Digital labor for Desktop Engineering

The desktop engineering involves managing and securing desktops and other endpoints within an organization’s environment.  We might have come across cloud-based and on-premises based management solutions like Intune, Workspace One UEM, Ivanti, Hexnode Configuration manager and many more. These solutions provide support for wide range of devices, including phones, tablets, PCs, IoT devices and more. Through centralized management console administrator can do device management, app management, policy management, image management, update and patch management, takes remote actions and many other administrative and support activities.

The integration of new digital labor aka agentic ai in desktop engineering would helps to solve complex technical problems, ensuring system stability and optimizing endpoint management. Some of the operational tasks where agentic ai can help the L2 and L3 support engineer such as software deployment and patch management, Microsoft office updates and patches can be tested, validate [with predefined rules and ticks] and deploys it on the endpoints. Also it can take precautionary measures for deployment failures. The proactive approach enhances system security while reducing manual efforts for IT teams.

Similarly for Windows autopilot tasks can also be assigned to agentic ai, where it can help in creating new profiles by feeding the information such as deployment mode, device groups, enrolment and registration. Agentic ai can streamline the autopilot deployment process and also helps administrator to discover vulnerabilities and exposure i.e. vulnerabilities remediation and through actionable models assists with remediation efforts.

Agentic ai also helps in application packaging and distribution. With the initial intervention by the administrator ai can adopt application packaging by automated process, optimized compatibility and ensures seamless deployment at the endpoints. One of the example automated application discoveries such as identify the required software components, dependencies and compatibility requirement before the packaging. Giving an use cases in office 365 application ai can scans existing configurations, detected any missing runtime libraries viz .NET framework, Visual C++ and it ensures the required dependencies are included in the packages.

Post packaging AI can help in optimizing the packaging to provide better performance also does the compatibility testing making sure works across different Windows environments and distribute the packages to endpoints.

Also, agentic ai can be ingrained for rollback mechanism so that it ensures smooth recovery in case of installation failure. Microsoft is actively developing agentic AI capabilities for Windows, including Model Context Protocol (MCP), It enables AI agents to interact with native Windows applications.

 MCP framework follows a client-server architecture, where a host application connects to MCP servers that provide access to tools and data. This framework enhances application packaging by allowing AI-driven automation and optimization. The integration accelerates the deployment of advanced AI solutions, enhancing efficiency and empowering end-users with smarter, more intuitive technologies..

Virtual Desktop Deployment and Management through Agentic AI

Many organizations have deployed virtual desktops to provide remote connectivity to their employees or contractors by delivering requires resources such as desktops and applications. Before we bring the touch of new technologies to the platform, it is recommended that modernize the existing/legacy infrastructure. Such as legacy platform on-premises desktops, static file share, application delivery are needs to be replaced by Cloud-first solutions that are agile, scalable and easier to manage.

The integration of Agentic AI enhances Virtual Desktops Infrastructure by automating most of the management tasks, helps in optimizing the infrastructure and improvising the overall performance.

The agentic ai running with the platform can perform most of administrative tasks such as dynamically adjust CPU, memory and storage based on the user requirements, also ensuring the efficient utilization of the resources. Also, agentic ai can provide self-healing mechanisms detect and resolve any performance bottlenecks. AI can strengthen security enforcement, monitoring threats and anomalies and ensure that’s environment is compliant and provides the insights during the audits.

Some of the repetitive tasks such as desktop provisioning and application deployment to certain users’ groups can be automated and handled by agentic ai, also without manual intervention update images and update the catalogs or session host pools.

Additionally, it streamlines session management, assist administrator in optimizing end-user experience. Agentic AI makes desktop environment more resilient, adaptive and cost effective, improving productivity and operation efficiency.

Conclusion

Agentic AI is transforming the digital workplace by simplifying tasks, automating processes, and enhancing efficiency. It takes care of routine/repetitive work, resolves technical issues, strengthens security, and supports employees, allowing teams to focus on meaningful projects or business strategies. By adapting to changing business needs, AI ensures smoother operations, better collaboration, and increased productivity.

As companies grow, Agentic AI helps them stay competitive and future-ready, making work smarter and more seamless. they gain a more intelligent, proactive, and scalable work environment. In essence, it acts as a reliable digital assistant, keeping the workplace efficient, secure, and optimized for success.

“Embracing Agentic AI is no longer just an innovation—it’s a strategic necessity for future-ready digital workplaces”

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