### AI Agents Need Governed Enterprise Data

AI agent data governance is becoming a core requirement for enterprise AI success. In this Techstrong.ai Leadership Insights episode, Mike Vizard talks with Tim Bond, Chief Product Officer at Adeptia, about why many AI agent projects stall when they reach real enterprise systems. The issue is not only model quality. It is also the quality, structure, access, and governance of the data those agents need to use.

Bond explains that modern cloud-first companies may move faster with AI agents. Many other organizations still depend on legacy systems, older integration patterns, and fragmented application environments. Those systems may not expose clean APIs. They also may not support newer approaches such as Model Context Protocol, or MCP. That makes it harder for agents to connect, understand available capabilities, and safely take action.

### Data Products Create a New Access Layer

The conversation explores why companies may need a new data product layer for agentic workflows. Traditional data warehouses and data lakes were built mainly for analytics. AI agents need something different. They need governed operational data that can support reading, reasoning, and writing back to systems.

Bond describes data products as a way to normalize information from multiple enterprise systems. This layer can help agents work across ERP, CRM, mainframe, and other business applications. It can also reduce the chaos that comes from every platform vendor offering its own agent. For multi-step workflows, enterprises may need agents that operate across systems instead of staying inside one vendor stack.

### Context, Cost, and Control Matter

AI agent data governance also affects cost and context engineering. Bond notes that tokens are now on the CFO’s radar. Enterprises need to decide when an AI agent should make a decision and when deterministic code should execute the workflow. That approach can reduce cost while improving reliability.

The episode also looks at why agents should not simply inherit every permission from a human user. Agents can act at machine speed. They can also take data at face value. That makes clean data, granular access, and strong governance essential.

For IT, data, and AI leaders, the takeaway is clear. Enterprise AI will depend on more than adding agents to existing systems. Organizations need better integration, clearer data ownership, and governance models designed for autonomous software. Without that foundation, AI agents may expose the same data problems companies have avoided for years.