Learn the key risks and challenges of deploying agentic AI in enterprises and discover how to address them with governance and observability.

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Agentic AI systems that autonomously plan, decide, and act introduce significant enterprise risks including identity sprawl, tool misuse, data leaks, and limited observability. Unlike traditional AI, agents pursue outcomes by orchestrating tools and APIs in real-time, creating complex governance challenges. Key mitigation strategies include treating agents as non-human identities with lifecycle governance, implementing production-grade telemetry and human-in-the-loop checkpoints, enforcing least-privilege access controls, and anchoring programs to frameworks like NIST AI RMF. Organizations should start with bounded workflows, instrument everything from prompts to tool I/O, and build capabilities incrementally while maintaining strict observability and audit trails.

Agentic AI risks and challenges

Key risks of agentic AI in enterprise environments

How leaders should adopt agentic AI responsibly

Practical controls that reduce agentic AI risks

What strong agentic AI governance looks like in action

How Domino enables safe and governed agentic AI adoption