AI agents are moving into production environments across hospitals and factories, but enterprises lack the infrastructure to manage them securely. Medical transcription agents now update electronic health records and surface patient history in real time. Computer vision agents run quality control on manufacturing lines faster than humans. The problem: these agents generate non-human identities that most companies cannot track, limit in scope, or revoke at machine speed.
Identity and access management (IAM) systems were designed for human workers. They assume periodic login attempts, clear role hierarchies, and manual credential management. AI agents operate continuously, need dozens of API connections, and generate thousands of authentication events per hour. Traditional IAM tools simply cannot keep pace.
Cisco President Jeetu Patel stated at RSAC 2026 that 85% of enterprises are running agent pilots while only 5% have reached production maturity. Identity governance is the bottleneck, not model capability or compute resources. Enterprises cannot inventory which agents exist, cannot scope their permissions correctly, and cannot revoke access instantly if an agent malfunctions or gets compromised.
The security implications are severe. A medical transcription agent with overly broad database access could expose patient records. A manufacturing agent with unchecked permissions could disable safety systems. Without real-time identity revocation, a compromised agent could operate undetected for days.
Solving this requires new IAM architectures built from the ground up for machine actors. Systems must auto-discover agent identities, enforce granular permission boundaries, audit every API call, and revoke access in milliseconds rather than minutes. Some vendors are building agent-specific IAM tools, but adoption remains fragmented.
Until enterprises solve the identity problem, most AI agents will stay in controlled pilots rather than drive production workflows. The technology works. The security framework does not.