Agentic AI identity diverges across company, employee, and peer agents

At a glance

What this is: This analysis breaks AI agents into company, employee, and agent-to-agent categories and concludes that each demands different identity and access controls.

Why it matters: IAM teams need to distinguish agent types because inherited permissions, cross-system execution, and delegated decision-making change how least privilege, governance, and auditability must work.

👉 Read C1.ai's analysis of the divergent agentic AI identity landscape

Context

Agentic AI identity is emerging as a governance problem because AI agents are not all the same. Some behave like app-embedded service accounts, some act on behalf of users across multiple systems, and some negotiate access with other agents in real time, which makes old identity assumptions too narrow for practical control.

The article’s central point is that traditional IAM was built around relatively static identities with predictable lifecycles. Agentic AI introduces identities that can be short-lived, dynamic, and action-oriented, so security teams need to rethink authorization, permission scope, and oversight before those behaviours become embedded in production workflows.

Key questions

Q: How should security teams govern personal AI assistants that act on behalf of employees?

A: Treat each assistant as a distinct non-human actor with its own identity, policy scope, and audit trail. Human delegation alone is not enough when the assistant can move across email, documents, calendars, and internal systems. Governance should bind the sponsor, the executor, and the target resource so access reviews and investigations can separate request from action.

Q: Why do agent workflows create more governance risk than standalone models?

A: Agent workflows create more governance risk because they combine reasoning with action. Once tools, memory, and handoffs are involved, a failure in one step can affect later decisions or external actions even if the base model seems safe. Governance must therefore cover execution paths, not just outputs.

Q: What breaks when AI agents are given broad inherited permissions?

A: Broad inherited permissions break the assumption that access is tied to a narrow business need. The result is larger blast radius, weaker accountability, and faster propagation of mistakes or abuse across connected systems. A single compromised or misconfigured agent can then touch far more data and workflows than the original task required.

Q: How do IAM teams know whether agentic AI is actually under control?

A: Look for evidence that every agent identity is discoverable, every session has a clear end point, and every high-risk action is observable in context. If the team cannot trace credentials from issuance to retirement, or cannot explain unusual tool use, the programme is not yet governing agentic identity.

Technical breakdown

Company AI agents as app-scoped service identities

Company AI agents sit inside a specific business application and act on behalf of the organisation, often in ways that resemble service accounts with broader decision-making. Their identity is tied to the application boundary, not to a person, which means permissions can accumulate quickly across SaaS environments. The technical challenge is not that they exist inside one app, but that each SaaS product may introduce its own agent with its own action surface and audit trail. That creates a distributed identity estate that looks familiar on paper but behaves differently at runtime.

Practical implication: Treat app-embedded agents as governed non-human identities and review their entitlements as part of application ownership.

Employee AI agents and delegated user permissions

Employee AI agents operate across tools on behalf of a human user, which means they inherit parts of the user’s access model while also acting independently within that scope. This creates a sub-user problem: the agent is not the user, but it is also not a fixed service account with one purpose. The technical tension is between productivity and permission boundaries, because inherited access can become broader than the task requires. Without a way to constrain delegated actions, the agent becomes an access amplifier rather than a helper.

Practical implication: Separate user access from agent delegation so the agent’s permission set is narrower than the human’s baseline access.

Agent-to-agent interactions and dynamic trust

Agent-to-agent workflows introduce a different control problem because one machine identity is asking another for data or action in real time. The question is no longer just whether the agent is authenticated, but whether its request is authorised in the context of a changing workflow. That requires dynamic trust decisions, clear data-flow boundaries, and auditability across multiple identities that may chain decisions without human review. The more agents collaborate, the harder it becomes to explain which identity made the decisive call and under what delegated authority.

Practical implication: Define authorisation boundaries for inter-agent calls before allowing agents to exchange data or trigger downstream actions.

Threat narrative

Attacker objective: The objective is to exploit delegated AI authority so actions appear legitimate while the agent performs more access or more impact than intended.

1. Entry begins when an organisation gives an AI agent legitimate access to applications, data, and tools so it can perform delegated work. Escalation follows when inherited permissions or cross-system trust allow the agent to act beyond the narrow task it was meant to complete. Impact occurs when chained agent actions move data, approve transactions, or alter business state without sufficient human review or clear auditability.

Breaches seen in the wild

• Meta AI Instagram Account Takeover — 20,225 Instagram accounts hijacked via compromised Meta AI support chatbot with overprivileged access.
• Replit AI Tool Database Deletion — Replit vibe coding AI assistant deletes live production database and creates 4,000 fake user records.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Agentic AI identity is not a new branding layer on top of IAM. It is a different operating model that breaks the assumption that identities are stable enough to be governed through static provisioning alone. Once an agent can act across tools and make runtime choices, authorization becomes behavioural rather than purely declarative. The implication is that identity architecture must distinguish between human intent, delegated machine action, and autonomous execution.

Company agents are effectively high-powered non-human identities with application-local blast radii. The governance risk is not just over-permissioning, but multiplicative sprawl, because every SaaS platform can create its own agent surface. That makes application ownership a security control, not just an operational one. Practitioners should treat embedded agents as part of the service identity estate, not as a feature request from the software team.

Employee agents expose a sub-user governance gap. Traditional access models can describe what the human may do, but they do not cleanly describe what a delegated agent should do on the human’s behalf. That gap becomes acute when the agent can cross systems and assemble actions from multiple permissions. The practical conclusion is that delegated access must be designed as a separate policy object, not assumed to inherit safely from the user.

Agent-to-agent workflows create accountability debt unless every trust edge is explicit. When one agent requests another agent’s data or execution, the security question becomes who authorised the chain, not just who signed in. That shifts focus from identity proofing to runtime trust and audit integrity. Organisations that let agents collaborate without deterministic policy boundaries will struggle to explain decisions after the fact.

Ephemeral agent behaviour invalidates the premise that access review alone can govern AI identities. Access review was designed for identities that persist long enough to be observed and recertified. That assumption fails when the actor is dynamic, task-scoped, and capable of changing context across a single session. The implication is that governance must move closer to the transaction layer, where agent intent and access scope can be constrained in real time.

From our research:

• 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface, according to Ultimate Guide to NHIs.
• Only 5.7% of organisations have full visibility into their service accounts, which explains why delegated machine access is so often under-governed.
• This also links to Ultimate Guide to NHIs , 2025 Outlook and Predictions, where agentic growth is framed as a governance and lifecycle problem, not just an AI rollout issue.

What this signals

Agentic AI will force IAM teams to separate identity proof from runtime authority. The programme question is no longer whether the agent logged in, but whether the action it took was within a policy boundary that still makes sense after the workflow changed. That pushes practitioners toward transaction-level controls and away from identity checks that stop at session start.

Company agents will expand the service identity estate faster than most organisations can inventory it. Every SaaS platform that adds its own AI actor increases the number of machine identities, permissions, and audit sources that security teams must manage. The practical response is to align application ownership, identity governance, and logging before the sprawl becomes invisible.

For practitioners

• Classify AI agents by operating model Separate app-embedded company agents, user-delegated employee agents, and agent-to-agent workflows before assigning ownership, policy, and audit controls. Different agent types need different entitlement boundaries and review cadences.
• Constrain delegated permissions below human baseline access Build policy so an employee agent receives only the minimum permissions required for its task, even if the human user has broader rights. Do not let productivity tooling inherit user access by default.
• Inventory agent trust edges across systems Map every place an agent can request data, trigger actions, or hand off to another agent, then require explicit authorisation for each edge. This is the control surface that determines whether agent collaboration is governable.
• Tie agent auditability to business transactions Log the task, data source, downstream action, and policy decision together so investigators can reconstruct why an agent acted. Transaction-linked logging is more useful than generic event capture for agentic workflows.

Key takeaways

• Agentic AI creates distinct identity models for app-scoped, user-delegated, and inter-agent workflows.
• The main risk is not AI itself but permission inheritance, cross-system trust, and weak accountability for delegated actions.
• IAM programmes need policy and audit controls that operate at the transaction layer, not just at sign-in or provisioning time.

Key terms

• Organizational AI Agent: A shared autonomous software entity that acts on behalf of multiple users or workflows. Unlike a personal assistant, it usually has persistent access to systems and data, which makes it a non-human identity with operational authority and a larger governance footprint.
• Employee AI Agent: An AI agent that acts on behalf of an individual user across multiple tools and systems. Its identity is delegated from the human, but its actions can exceed what the user would do manually, so access policy must be narrower and more explicit than the person’s own rights.
• Agent-to-Agent Chain: A sequence of autonomous software entities handing work, context, or requests from one agent to another. Each hop can widen the trust boundary, weaken identity context, and create unreviewed access to systems or data if one link is unregistered or misconfigured.
• Delegated Machine Access: Access exercised by a non-human actor on behalf of a human or another system. The important issue is not only who requested the access, but how far the delegated actor can chain actions once runtime execution begins.

What's in the full article

C1.ai's full blog covers the operational detail this post intentionally leaves for the source:

• The article’s full breakdown of the three agent types and the operating model differences between them.
• The vendor’s examples of how company agents, employee agents, and agent-to-agent flows map to real enterprise workflows.
• The source post’s discussion of why current IAM assumptions around static identities and long-lived access do not fit agentic systems.
• The article’s own framing of future authorization frameworks for granular, task-specific permissions.

👉 C1.ai's full post adds the agent taxonomy, governance implications, and future authorization requirements in one place.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an identity security programme, it is worth exploring.