Cognizant has announced a major investment in agentic AI with plans to deploy 1,000 context engineers over the next year, marking a milestone in the industrialization of context engineering as a core discipline for enterprise AI adoption.
Ravi Kumar S, CEO of Cognizant, said: “By training 1,000 context engineers and equipping them with Workfabric AI’s ContextFabric platform, we are helping our clients move beyond experimentation toward scalable AI adoption.”
A recent Gartner report anticipates a rapid rise in enterprise integration of agentic AI: by 2028, 33 percent of enterprise software applications will include agentic AI capabilities — up from less than 1 percent in 2024 —and at least 15 percent of everyday work decisions will be made autonomously by AI agents.
However, more than 40 percent of current agentic AI projects are expected to be canceled by the end of 2027, mainly due to high costs, unclear business value, and pervasive “agent washing,” where vendors mislabel AI assistants as true agentic systems.
Meanwhile, a recent IDC Blog revealed that generative and agentic AI are already reshaping enterprise applications: over 50 percent of the market now features AI assistants or AI advisors embedded in solutions, and around 20 percent of applications are incorporating full-fledged AI agents that perceive, evaluate, and act autonomously.
IDC finds that more than 80 percent of surveyed companies view “AI agents as the new enterprise apps,” prompting a major shift in investment priorities. They also anticipate that agent-driven interfaces—text and voice prompts—will increasingly replace traditional UI, with full agent-led applications replacing discrete functions over the coming years.
As part of this initiative, Cognizant is partnering with Workfabric AI, whose ContextFabric platform transforms enterprise workflows, processes, data, and governance systems into actionable intelligence for AI agents. By providing a continuous runtime grounding layer, ContextFabric helps ensure AI systems remain aligned with organizational execution patterns, compliance standards, and business objectives.
Rohan N. Murty, CEO of Workfabric AI, emphasized: “Cognizant’s commitment, starting with 1,000 context engineers, is a bold signal of where the services industry is headed. ContextFabric will be the force multiplier that turns this vision into reality.”
Context Engineering: The Next Frontier for AI
According to Cognizant, context — defined as the sum of an enterprise’s operating models, roles, goals, metrics, processes, and governance frameworks — is the key enabler of the large language model (LLM) era. Context engineering ensures that AI agents:
Represent enterprise knowledge
Align with human goals and policies
Adapt to real-time challenges
Deliver outcomes that reinforce trust and compliance
“Every technology shift creates a services shift. In the LLM era, the lever is context,” said Ravi Kumar S, CEO of Cognizant. “By training 1,000 context engineers and equipping them with Workfabric AI’s ContextFabric platform, we are helping clients move beyond experimentation toward scalable AI adoption.”
Scaling Agentic AI with ContextFabric
Workfabric AI’s ContextFabric platform has already demonstrated measurable enterprise benefits, including up to 3X higher accuracy, 70 percent fewer hallucinations, faster deployment cycles, and stronger ROI.
Cognizant’s context engineers, supported by the company’s Agentic Development Lifecycle (ADLC), will focus on:
Capturing enterprise knowledge and execution patterns
Managing the full context lifecycle with governance and security
Building integration pipelines for retrieval, synthesis, and distribution of context
Packaging reusable “context packs” for scalable deployments
Designing blueprints for industry-specific agentic AI use cases
Enterprise Impact and Competitive Advantage
Through this initiative, Cognizant aims to help enterprises move beyond AI pilots to full-scale deployments. Key benefits include:
Risk Reduction: Ensuring compliance with industry standards and regulations
Higher ROI: More accurate and trusted AI agents that increase adoption
Efficiency Gains: Reduced errors and rework
Cost Optimization: Streamlined architectures and reusable context assets
Faster Time-to-Value: Accelerated deployment cycles
Differentiation: Context as a strategic advantage linking company vision to execution
With this initiative, Cognizant is positioning itself as a leader in enterprise agentic AI by combining domain expertise, industry insights, and cutting-edge context engineering capabilities to accelerate AI-driven transformation at scale.
Rajani Baburajan
