Real world applications of agentic AI for next generation IT services
By Verity Greig, Solution Lead at Tata Consultancy Services, specialising in AI‑driven transformation and next‑generation IT operations.
Agentic AI is emerging as the most transformative shift since cloud computing, moving operations from reactive management to intent-driven autonomy. The opportunity: higher reliability, faster resolution, and meaningful cost takeout, all whilst freeing teams from routine tasks to focus instead on innovation, complex problem-solving, and the delivery of higher-level business value.
Last year was the year where we started to see agents working autonomously, often over long tasks, making decisions, using tools and even other agents to reach a goal. Building on this momentum, 2026 is poised to be the year when Agentic AI takes over repetitive and time-consuming tasks in full, enabling teams to focus their talents on strategic initiatives.
Agentic AI can achieve a goal with limited supervision where AI agents mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask with the multi-step reasoning required to reach the goal while agent efforts are coordinated through AI orchestration. This approach is ideal for enterprise IT teams managing parallel tasks to deliver digital services that support organisational goals.
What are Next Generation IT Services? Next Generation IT Services are modern, AI-first and observability-led, delivering reliability, agility, and improved staff and customer experience at scale. Next Generation IT Services maximise existing organisational investments, harness native AI capabilities within existing platforms, and combine these two foundational elements with rapid agent development. This transformation must be underpinned by secure-by-design principles and clear guardrails to ensure trust, safety, and compliance. It is not just about implementing AI tools, but about reimagining how organisations will operate differently.
Next Generation IT Services evolve along AI Maturity Levels. This ranges from the basic level where AI is used as a tool and the human does all the work, moving to the use of AI as an assistant, then AI as the worker, and finally an AI-driven workforce where the human becomes the governor.
Through this blog article, I would like to show you how you can realise a vision that leverages Agentic AI to create autonomous, self-managing, and predictive IT and network operations, thereby minimising or eliminating human intervention in routine operational tasks.
Agentic AI is enabling next generation cloud hosting by shifting operations from reactive management to autonomous, intent driven execution. In real world environments, agentic AI supports predictive incident prevention, automated remediation, intelligent capacity and cost optimisation, compliance enforcement, and continuous reliability engineering across hybrid and multi cloud estates. This directly impacts roles such as cloud operations engineers, SREs, platform engineers, service desk analysts, security operations teams, and FinOps practitioners, shifting their work from manual execution to supervision and optimisation. Rather than these teams managing dozens of discrete agents, agents can be coordinated through a central orchestration layer integrated with platforms such as Azure AI Foundry, AIOps, and ITSM. This allows teams to manage outcomes and intent, not individual agents, freeing capacity for higher value engineering and innovation.
Agentic AI plays a critical role in readying data for AI by automating discovery, ingestion, enrichment, and governance across complex enterprise data estates. Agentic systems can combine metadata, domain knowledge, and enterprise context through cognitive knowledge fabrics and RAG patterns, leveraging platforms such as Databricks with Genie, Snowflake Cortex, Microsoft Fabric, and vector databases to create trusted data. Building on the virtuous circle I described in my 2025 blog, AI is applied back onto the data layer through autonomous data engineering using tools such as Amazon Q, cloud data copilots, and GenAI assisted pipelines. Insights are presented through natural language interfaces, adaptable dashboards, charts, and embedded visual analytics generated by agents, enabling decision intelligence that can describe, predict, and prescribe outcomes in near real time.
In networking, Agentic AI is transforming NOC operations from reactive monitoring and predefined automation to autonomous, intent driven control. Unlike rule based or logic driven solutions that respond to known conditions with scripted actions, agentic systems continuously reason over live telemetry, historical context, and business intent to determine the most effective response. Uses span real time fault detection, event correlation, root cause analysis, and adaptive remediation across network domains. Commonly used tools include Cisco ThousandEyes and Cisco AI Network Analytics, Juniper Mist AI, Dynatrace, and Azure Monitor with AI driven insights. NOC teams shift from alert handling to supervision, optimisation, and exception management through centrally orchestrated control planes.
Agentic AI is transforming End User Services by moving from scripted automation and assistive GenAI to autonomous, outcome driven execution. Unlike traditional tools that follow predefined workflows or generate recommendations, agentic systems can reason, coordinate, and act across multiple platforms to achieve an end-goal. In practice, agents enable zero touch provisioning that adapts to user context, autonomous joiner mover leaver orchestration across identity and devices, ticket resolution that investigates root cause and validates success, and self healing that predicts and prevents experience degradation. Platforms include ServiceNow agentic workflows, Microsoft Intune and Autopilot, Nexthink, Amazon Connect, and enterprise virtual assistants.
In ADMS, Agentic AI is enabling agile application development, continuous enhancement, and seamless integration across legacy and modern platforms. Agentic AI can support requirements analysis, code generation, testing, defect prediction, and release automation. Agentic frameworks such as LangChain and CrewAI enable multiple specialised agents to collaborate across the SDLC, coordinating design, build, test, and deploy activities under shared governance. Integrated with DevSecOps pipelines, these agents accelerate delivery, improve quality, and reduce change risk, delivering 30–50% faster development cycles and ~40% faster release cycles, with further gains to 45–70% productivity uplift and 40–60% end to end flow automation as agents continuously learn from outcomes and the underlying agentic technology evolves and improves over time.
Agentic AI enhances Service Management and Service Integration by orchestrating work across multiple IT providers to deliver consistent end to end service assurance. Using platforms such as ServiceNow Now Assist, Agentic AI workflows, ITOM, and Performance Analytics, agents can build to continuously correlate incidents, changes, events, SLAs, XLAs, and supplier performance data. Agentic AI automates cross provider incident swarming, root cause analysis, impact assessment, and remediation coordination under shared governance and policies. This enables SIAM teams, service owners, and vendors to move from reactive, tool driven reporting and escalation to proactive control, faster resolution, improved accountability, and measurable business value across complex multi supplier environments.
Agentic AI is redefining Security Operations by autonomously investigating security goals end to end, planning multi step response strategies, and executing actions across security tools under bounded autonomy. Agentic systems decide what to do next based on evolving evidence, coordinate and sequence containment and remediation across SIEM, endpoint, identity, cloud, and network controls, and verify outcomes before escalating exceptions to analysts. For example, CrowdStrike reports its Charlotte AI Detection Triage achieves over 98% triage accuracy and removes 40+ analyst hours per week, accelerating response to real threats. Darktrace’s autonomous response has publicly helped UK organisations blunt ransomware in the wild, including during the WannaCry outbreak, where Antigena was able to interrupt ransomware activity and limit disruption across hospital environments.
Securing the agentic layer itself is also essential: enforce strong identity and least‑privilege for agents, policy guardrails, audit logging, human‑in‑the‑loop controls, and model governance to prevent misuse or prompt‑injection style attacks.
The Future – What does this mean for IT Services?
Agentic AI can deliver ~25–35% total IT run‑cost savings across all towers within 12–24 months, with some towers achieving 40%+ efficiency or cost reduction where automation maturity is high.
The traditional Tower Structure will need to evolve as teams integrate AI agents into workflows and operations. Architecture needs to be modular, built for observability and managed by agile teams ready to operate at speed. IT organisations will need to shift to a model where humans and agentic AI work together in continuous learning loops, with AI embedded across architecture, delivery, and operations. Teams will need experience in RAG techniques, integrating agentic AI with existing systems, multi-agent orchestration, change management and responsible AI security and governance.
This period of transformation presents significant challenges, and IT services organisations are well-prepared to leverage their expertise in delivering AI-powered managed services, ensuring a seamless transition toward an AI-powered workforce.
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Authors
Verity Greig
I’m a Leader who believes that technology can be a force for good for Public Services. As a Cloud Evangelist, I have the privilege of leading TCS' cloud strategy for the UK Public Sector. Recently TCS was named the Number One IT Services Provider in the UK by TechMarketView. A moment of pride for us but also a moment of obligation. Prior to that I was an Azure Product Manager at Microsoft tailoring the Azure service to meet Public Sector needs. As the mum of two daughters, I’m particularly passionate about inspiring more women into STEM industries.
