### AI Agent Governance Starts With Risk

AI agent governance is becoming a must-have discipline as enterprises move from experiments to production deployments. In this Techstrong.ai Leadership Insights episode, Mike Vizard talks with Harshil Shah, Head of Generative AI at R Systems, about the risks that emerge when agents gain more autonomy across business systems.

Shah explains that agent risk is not limited to prompt injection or data leakage. Organizations also face strategy risk when projects fail to deliver measurable outcomes. They face cost risk when every request is routed to expensive frontier models. They also face operational risk when agents are given broad access without enough oversight.

### Open Models, Costs, and Business Outcomes

The conversation looks at the growing role of open weight models in enterprise AI. Shah notes that open models can be air-gapped, self-hosted, fine-tuned, and observed more directly. That can make them useful for production agents when cost, control, and compliance matter.

At the same time, he cautions that enterprises need a clear strategy. AI agents should not be judged only by whether they work in a demo. They need to create measurable business value. They also need to be promoted through levels of autonomy over time. That approach lets organizations start with narrow tasks, learn from results, and scale without overspending.

### Permissions Must Be Built Into the Tools

AI agent governance depends on more than instructions in a system prompt. Shah argues that guardrails should be enforced through tools, connectors, identities, roles, and permissions. If an agent is not allowed to access salary data, that restriction should exist in the governed tool layer, not just in a polite prompt.

The episode also explores why agent observability matters. Enterprises need monitoring, alerts, and kill switches before agents run across many systems. They also need human-in-the-loop approval for high-risk actions.

For IT, security, and AI leaders, the message is clear. AI agent governance must be designed before agents become deeply embedded in workflows. Strong foundations around identity, access, cost, and accountability will help enterprises scale agents safely while still gaining the productivity benefits of automation.