as ai agents move from proof-of-concept into production, a new set of challenges are emerging. one of them is identity and access management for these non-human actors.
today, every employee at a company gets a user profile, a set of credentials, and carefully scoped permissions—often managed by sophisticated rbac (role-based access control) systems. but what about the ai agents that are now reading your emails, updating your crm, or querying your database?
if agents are to become true digital workers, they’ll need to be treated like employees: with profiles, audit trails, and—critically—permissions. otherwise, we risk creating a shadow workforce with no accountability, no oversight, and massive security risks.
why agent identity matters
agents increasingly act on behalf of users, teams, or entire organizations. if agents are anonymous or over-permissioned, they become a new vector for data leaks, fraud, and compliance failures. just as with human employees, we need to know: who did what, when, and why?
tl;dr, these are the reasons why agent identity and access management are critical:
- audit trails: every agent action should be traceable.
- accountability: agents must operate within clear boundaries.
- compliance: regulations may soon require agent identity management.
agent profiles: the new user accounts
think of an ai agent profile as a digital employee file:
- unique agent identifier: how the agent is recognized in the system
- credentials (api keys, oauth tokens, etc.): what the agent uses to authenticate to services
- capabilities: what the agent is allowed to do
- owner/supervisor: who created or manages the agent
- context: purpose, current task, environment
agent profiles will enable better management, trust, and lifecycle control (onboarding, offboarding, suspension).
rbac for agents: roles, permissions, and fine-grained access
assigning roles and permissions to agents is not going to be a nice-to-have—it will be a necessity. but the bar is even higher than for humans:
- least privilege: agents should only access what’s absolutely necessary.
- dynamic permissions: as agents learn or change roles, their access must update in real time.
- revocation: removing agent access instantly is critical for security.
fine-grained data access: beyond the row, down to the cell
in many organizations, access controls are not just at the file or table level—they’re at the row or even cell level. for example, a sales agent may only see revenue data for their region, or a healthcare agent may see only certain fields in a patient record.
ai agents will need to respect these boundaries:
- cell-level rbac: agents should only read/write the specific data they’re authorized for.
- context-aware policies: access rights may depend on the agent’s task, user, or even time of day.
- auditability: every access—especially to sensitive data—must be logged and reviewable.
the opportunity: building the agent identity layer
just as okta and auth0 built massive businesses around human identity, there’s a coming wave of startups building identity, rbac, and lifecycle management for agents. we’ll see:
- agent directories (who are the agents in my org?)
- permission dashboards
- automated onboarding/offboarding
- delegation and escalation workflows
challenges and open questions
- how do you revoke agent access instantly, everywhere?
- how do you handle agent-to-agent delegation and impersonation?
- what about agents that spawn other agents—who is responsible for their actions?
- how do you ensure explainability and transparency as agents become more autonomous?
other open questions
- user consent: how do users grant (and revoke) agents permission to act on their behalf?
- agent lifecycle: what happens to access and data when an agent is retired or replaced?
- cross-org collaboration: how are permissions managed when agents work across company or department boundaries?
- human-in-the-loop: when should humans be able to override or audit agent actions in real time?
- privacy: how do we ensure agents only access the minimum data needed, especially with sensitive info?
- impersonation risks: how do we prevent fake or hijacked agents?
- regulation: how will new laws and liability shape agent identity and access?
these are just a few of the interesting topics that will shape how we trust and deploy ai agents at scale. we've already dealt with these issues in the human world, and the same principles will apply to agents, but with even more complexity.
conclusion
as organizations deploy more ai agents, the need for clear identity and access controls will only grow. the best solutions will balance security, flexibility, and transparency—without getting in the way of what makes agents powerful in the first place.