The defining characteristic of an enterprise AI agent isn't its intelligence, but its specific, auditable permissions to perform tasks. This reframes the challenge from managing AI 'thinking' to governing AI 'actions' through trackable access controls, similar to how traditional APIs are managed and monitored.

As AI evolves from single-task tools to autonomous agents, the human role transforms. Instead of simply using AI, professionals will need to manage and oversee multiple AI agents, ensuring their actions are safe, ethical, and aligned with business goals, acting as a critical control layer.

Traditional identity models like SAML and OAuth are insufficient for agents. Agent access must be hyper-ephemeral and contextual, granted dynamically based on a specific task. Instead of static roles, agents need temporary permissions to access specific resources only for the duration of an approved task.

Giving a new AI agent full access to all company systems is like giving a new employee wire transfer authority on day one. A smarter approach is to treat them like new hires, granting limited, read-only permissions and expanding access slowly as trust is built.

As AI agents take over execution, the primary human role will evolve to setting constraints and shouldering the responsibility for agent decisions. Every employee will effectively become a manager of an AI team, with their main function being risk mitigation and accountability, turning everyone into a leader responsible for agent outcomes.

Managing human identities is already complex, but the rise of AI agents communicating with systems will multiply this challenge exponentially. Organizations must prepare for managing thousands of "machine identities" with granular permissions, making robust identity management a critical prerequisite for the AI era.

Todd McKinnon conceptualizes AI agents not as simple tools but as a fundamentally new identity category. This identity possesses attributes of both a human user (roles, permissions) and a system (automation, being headless). This reframing is central to building the next generation of enterprise security and access management.

As autonomous agents become prevalent, they'll need a sandboxed environment to access, store, and collaborate on enterprise data. This core infrastructure must manage permissions, security, and governance, creating a new market opportunity for platforms that can serve as this trusted container.

The CEO of WorkOS describes AI agents as 'crazy hyperactive interns' that can access all systems and wreak havoc at machine speed. This makes agent-specific security—focusing on authentication, permissions, and safeguards against prompt injection—a massive and urgent challenge for the industry.