TCS is building a governance layer to manage enterprise AI agents

What happened?

Tata Consultancy Services is developing an enterprise AI governance layer designed to coordinate agents built by TCS, its customers and third-party technology providers. According to reporting by Financial Express on 12 October, the system is intended to act as a central control plane across enterprise platforms such as SAP, ServiceNow and Oracle.

TCS executive director and COO Aarthi Subramanian described the layer as a way to manage agents, coordinate their activities and control costs. The initiative reflects a problem enterprises face as they move from experimenting with individual AI assistants to deploying multiple AI systems across business operations.

The announcement comes after TCS reported that its annualised AI revenue had reached approximately $3.1 billion in the September quarter, an increase of about $500 million from the previous quarter. The annualised figure is a run-rate estimate, not revenue earned in that single quarter. Reuters separately reported that TCS maintained an operating margin of 24% despite its AI investments and a challenging technology-services market.

Why does this matter?

Imagine a bank using several AI agents:

• One agent reads customer requests.
• Another retrieves account or policy information.
• A third prepares a recommendation.
• A fourth updates an internal system after approval.

Without coordination, these agents might duplicate work, exceed their permissions, consume excessive computing resources or make inconsistent decisions.

An enterprise governance layer aims to help manage those risks. However, the announcement does not yet establish how effectively TCS’s proposed layer will perform across all these scenarios.

What this means for students and freshers

The opportunity is to learn how AI systems fit into real software architecture. Understanding prompt engineering alone is not enough to build dependable enterprise applications.

Useful skills include API integration, workflow orchestration, authentication, role-based permissions, logging, testing, evaluation and cost monitoring.

Practical project idea: Build a multi-agent customer-support application in which one agent retrieves information, another drafts a response and a human approves any consequential action. Include logs and tests that show what happens when an agent fails.

The Aiexl. perspective

The next generation of AI engineers won’t just build agents. They’ll need to make them work safely together.

Cover: AI-generated editorial illustration, not a photograph of TCS’s proposed system.