TCS Industrial Autonomy and Engineering Business Line Head: Scaling Agents Requires Domain Curation, Not Everyone Building Their Own
Sreenivasa Chakravarti, Global Head of TCS's (Tata Consultancy Services) Industrial Autonomy and Engineering business line, outlines the core contradiction in scaling AI agents within industrial value chains from pilots to widespread deployment: while large language models are sufficient for single-point pilots, cross-role and cross-departmental scale requires agents to align with organizational rules, technology stacks, and security compliance. His proposed 'Industrial Intelligence Foundry' organizes reusable agents by domain ontology, assembles applications by role, and treats agent lifecycle management itself as a delivered service. TCS claims this approach can reduce new product introduction time by 25% and improve manufacturing efficiency by 20-25%, but these are vendor-reported figures.
Large language models are sufficient to support a single AI agent pilot, but Chakravarti argues that the problem changes when enterprises attempt to deploy agents across different roles such as production line management, product management, and quality management. Each role experiences the agentic workflow differently, and more critically, if left unchecked, allowing every individual to create their own agents would cause those agents to operate outside the constraints of organizational rules, technology stacks, and security compliance.
TCS's response is the 'Industrial Intelligence Foundry': a repository of reusable agents organized by domain ontologies and classification systems. A quality manager needing to inspect visual checks in the production process can drag and drop an existing computer vision agent into their workflow; the same agent can also be reused by training or quality control departments. The more that is built, the thicker the accumulated knowledge base becomes, allowing the next application to stand on top of existing workflows.
Chakravarti positions the Foundry as an accelerator rather than a final product; TCS's core business remains the AI transformation of specific value chains. However, a notable shift is occurring: many clients now request that TCS manage the lifecycle of their agents on their behalf. He explains that each agent evolves rapidly, at a pace so fast that clients prefer to outsource ongoing maintenance. This implies that the traditional software delivery model of 'deploy and forget' is failing in the agentic context.