How Cognizant is rewiring Application Management for the Agentic AI era
For decades, enterprise application management ran on a simple, almost unquestioned logic: keep the lights on, and keep costs down. Support desks triaged tickets, engineers patched code, and IT leaders measured success in dollars saved per year.
Cognizant is now pulling that model apart and rebuilding it around a different unit of work: the AI agent. Rather than bolting automation onto existing support processes, the company is restructuring how applications are monitored, healed, modernized, and funded — with autonomous agents doing much of the load-bearing work that used to sit with human engineers.
The trigger, says Hari Parameswaran, SVP and Global Delivery Head for Application Development & Management Practice at Cognizant, is that the applications themselves have stopped behaving like static assets. “Today’s enterprise applications are not static assets to be maintained; they are living systems, continuously consuming data, generating decisions, and interacting with customers, partners, and regulators in real time,” he says. “An application failure today is not merely an IT incident. It has a direct business impact, whether on revenue, availability, or customer experience.”
That shift in stakes, Parameswaran argues, is what’s forcing the rewiring — pulling application management out of its cost-driven past and into an agent-led future built for resilience and speed.
Rewiring the operating model: from ticket queues to reliability engineering
The clearest sign of that rewiring is the rise of Site Reliability Engineering (SRE) as the operating model for application support. Where reactive break-fix teams once waited for something to go wrong, Parameswaran describes a world where AI agents are watching the system continuously — and increasingly acting on what they see.
“AI, automation, and observability help predict, prevent, and in some cases automatically resolve issues before they impact users, improving application resilience and business continuity,” he says. “AI agents increasingly handle initial triage and anomaly detection, while engineers focus on observability design, runbook orchestration, and post-incident learning.”
The upshot, in his framing, is that application management is being repositioned — not as overhead to be minimized, but as “a value driver, centered on platform reliability ownership, error-budget management, and AI-driven intelligent operations.”
Funding transformation from within
If AI is the engine of this shift, efficiency is the fuel — and Cognizant is betting heavily on a model it calls “self-funded transformation.” The idea: enterprises don’t need a separate transformation budget if they can extract enough value from how they already run their application estate.
“The essence of self-funded transformation lies in unlocking savings from the way existing applications are run today and reinvesting that capital into future transformation, effectively turning efficiency into fuel,” Parameswaran explains.
In practice, that starts with rationalization — cutting through years of accumulated technical debt. “Organisations have accumulated layers of technical debt embedded deep within the systems that run their business,” he says. “Each retired application removes layers of complexity, integration overhead, security risk, and ongoing support effort.”
Layered on top of that is AIOps, which Parameswaran says is quietly reshaping the economics of IT operations by shifting support “from reactive firefighting to predictive intelligence.” Even small gains in detection and resolution speed, he notes, “create valuable headroom that can be redirected toward resilient, platform-driven, and future-ready capabilities” — provided organisations are disciplined enough to reinvest those savings rather than let them “be absorbed into business-as-usual budgets.”
Beyond the pilot: AI hits the legacy core
Nowhere is that industrialization more visible than in legacy modernization — long the graveyard of ambitious enterprise IT programs. Parameswaran says the center of gravity has moved decisively past proof-of-concept work.
“We are seeing a decisive shift beyond pilots toward industrialized, factory-scale modernization,” he says. “AI now operates as a core execution layer across modernization factories spanning discovery, code transformation, and validation through orchestrated, autonomous agent ecosystems.”
The most striking gains, he says, are showing up in the systems enterprises have historically been most afraid to touch: mainframes and monolithic applications. “We’re delivering significant reduction in legacy application footprints with minimal disruption,” Parameswaran says. “This is not incremental uplift; it’s fundamental re-architecture.” He describes the trajectory bluntly: “from assistive AI to agent-led execution, enabling resilient, portfolio-wide transformation at enterprise scale.”
Zero-ops: destination or direction?
Ask Parameswaran whether “zero-ops” — fully autonomous, self-healing IT — is realistic, and he offers a careful yes-and-no. Autonomous operations, he says, are “increasingly becoming a business reality,” with observability, AIOps, incident response, and production operations already delivering measurable gains.
But he’s equally clear that the industry isn’t at the finish line — and that there may not be one. “We view Zero Ops, not as a destination but as an ongoing evolution,” he says. “Human oversight remains essential, as even autonomous systems require strategic direction, governance, and policy management.” He also flags a less-discussed risk of the rush toward automation: “enterprises must remain mindful of growing technical debts as AI solutions are deployed at scale.”
Why platforms are outperforming point tools
As enterprises weigh how to deploy AI across the application lifecycle, Parameswaran is candid about where he sees the real returns — and it isn’t in standalone copilots. “Platform-led Agentic AI is delivering better outcomes than standalone tools in application management and modernization, as evidenced by production data,” he says. “While standalone GenAI tools deliver productivity gains, platform-led multi-agent systems can drive significantly greater impact.”
He points to three pillars underpinning Cognizant’s own platform architecture: Connected Lifecycle Intelligence, for data flow across the software lifecycle; Multi-Agent Swarm Orchestration, enabling specialized agents to collaborate on shared context; and Goal-Oriented Autonomy, built on self-correcting reasoning loops.
The engineer’s new job description
This transition is reshaping roles as much as it’s reshaping systems. Parameswaran describes a workforce moving up the stack — from writing code to directing the AI that writes it. “Engineers are shifting from independently writing code to orchestrating AI-driven delivery, authoring intent specifications, validating AI-generated outputs, and owning quality rather than line-by-line authorship,” he says. Senior engineers, meanwhile, are moving into “AI-native architecture advisory roles, designing agent capability boundaries, human-agent interaction patterns, and guiding clients through enterprise AI transformation.”
New job titles are following the new job functions: Cognizant has begun building out roles including AI Architect, AI Platform Lead, Responsible AI Officer, and AI Delivery Enablement Engineer, alongside two recently introduced positions — Frontier Certified Engineer and Frontier Business Operator — created as part of what the company calls its AI Builder strategy. The company’s Skillspring platform, Parameswaran says, is designed to support that reskilling journey at scale.
India’s next act
Finally, Parameswaran turns to a question with implications far beyond any single company: what happens to India’s services industry as delivery shifts from headcount to autonomous agents? He sees not a threat, but “a defining opportunity.”
“India’s services leadership has been built on talent availability and readiness, complemented by strong technology expertise and deep domain knowledge,” he says. That domain fluency, built over decades of large-scale delivery, is becoming a differentiator in its own right: “This combination of domain expertise and contextual knowledge is becoming increasingly important in an AI-led, outcome-based world. It helps accelerate the development of AI agents and ensures that solutions are aligned with business outcomes across industries.”
He points to India’s more than 1,800 Global Capability Centers as evidence of a broader structural shift — from cost centers to something closer to R&D hubs. “These GCCs are no longer viewed as back-office centers; they are evolving into AI innovation hubs for global enterprises,” he says. “This reflects India’s growing role, not only in executing at scale but also in building, managing, and governing AI systems for enterprises globally.”