For decades, enterprise identity governance has answered a simple question:
“Who has access to what and why?”
That model worked when all actors were humans or static service accounts.
But today’s enterprise runs on a new kind of digital participant – AI agents that log into systems, retrieve data, and act on behalf of users.
Each of these agents:
- Connects using API keys or delegated credentials.
- Acts autonomously or semi-autonomously.
- Maintains state or “memory” across interactions.
- Can team up with other agents to achieve the objective
These are not background scripts anymore; they’re digital coworkers capable of reasoning and action.
That shift redefines how enterprises must think about accountability and compliance.
If an agent can update Salesforce records, trigger onboarding in Workday, or modify a security policy — who is responsible for that action?
Traditional identity governance systems track people and service accounts.
But AI agents blur those lines: they can act, learn, and persist like users yet operate faster, across boundaries, always up and without consistent oversight.
To preserve compliance and trust, enterprises must add a new layer of governance:
Agent Management by extending identity and access controls to intelligent, autonomous entities.
These three identity classes coexist under one truth:
Every actor — human, machine, or AI — must be owned, visible, and governed.
| Type of Identity | Description | Governance Maturity |
|---|---|---|
| Human | Employees, contractors, and partners who authenticate directly. | Mature: fully governed. |
| Service | Machine or API accounts used by systems. | Partial: non-person accounts tracked inconsistently. |
| Agent | AI entities that reason, decide, or act autonomously. | Emerging: requires new identity and risk frameworks. |
Managing human identities is a well-understood discipline. Most organizations already employ robust platforms and governance models for user lifecycle management, policy enforcement, and certification.
Service identities, often referred to as machine or non-person identities, are less mature but rapidly improving as organizations extend identity frameworks to APIs, workloads, and DevOps automation.
The Agent identity, however, represents the next frontier. Agents introduce reasoning, autonomy, and persistent decision-making capabilities that transcend traditional access models. Some will hold highly privileged entitlements, granting them access to sensitive systems or datasets — making their governance not optional, but essential.
Before exploring how to manage these digital actors, it’s critical to understand the diverse landscape of agents that enterprises will encounter. Each type exhibits unique behaviors, scopes of autonomy, and governance implications.
The modern enterprise doesn’t rely on a single kind of AI assistant. It operates within an ecosystem of agents — some lightweight and embedded within applications, others autonomous and cross-domain in nature. The table below outlines the ten distinct classes of agents that make up this new digital workforce.
Now that we understand the landscape of agent types, a natural question arises:
Should every one of these be managed like a digital employee?
The answer is: probably not.
Some agents, such as Embedded or Delegated copilots, operate entirely within the confines of their host applications. They don’t possess separate credentials, nor do they access external systems. These are essentially time-saving utilities that improve productivity but don’t carry independent security or compliance risk.
Examples:
- A Jira AI assistant that summarizes tickets.
- Smart Compose in Google Docs suggesting phrasing.
- A meeting-summary bot that reads transcripts but can’t modify data.
Other agents, however, clearly warrant tighter governance. Those that persist beyond user sessions, hold their own credentials, or access multiple systems or sensitive data behave much more like digital employees — and must be managed accordingly.
Finally, a few categories deserve top-tier attention.
These include Autonomous, Federated, and Meta-Agents — entities with the authority to act independently or even create other agents. These should receive full identity lifecycle management, continuous monitoring, and compliance oversight — the proverbial “red-carpet treatment” in your governance program.
So how do we determine which agents need lightweight tracking and which require full identity governance?
A practical approach is to assess each agent against four key dimensions.
| Dimension | Ask This Question | What It Tells You |
|---|---|---|
| 1. Autonomy | Does the agent act independently (plan, decide, or execute tasks) or only respond within an app context? | Defines behavioral type — Embedded → Autonomous. |
| 2. Persistence | Does it persist beyond one user session, or is it recreated each time? | Indicates whether lifecycle management is needed. |
| 3. Privilege Scope | What level of access does it have — single app, multiple systems, admin rights, or sensitive data? | Determines policy, audit, and certification requirements. |
| 4. Boundary | Is it internal (enterprise-built) or external (vendor- or partner-operated)? | Establishes contractual, compliance, and data-residency boundaries. |
By evaluating agents through these four lenses, enterprises can make informed, risk-based decisions about how each digital entity should be governed — from lightweight app helpers to fully managed, high-privilege AI identities.
Building on the classification logic outlined above, not every agent warrants the same level of oversight.
Some operate within well-contained application boundaries, while others act independently across multiple systems — behaving more like digital employees than mere tools.
In other words, the level of governance should match the level of autonomy, persistence, and privilege.
Below is my recommended framework for determining which agent types deserve their own identity and which can remain governed indirectly through existing controls.
Key Insight
Governance intensity should scale proportionally with an agent’s ability to act independently and its level of access.
- Agents that persist, reason, or cross system boundaries must be treated as first-class digital identities, complete with ownership, lifecycle management, and continuous monitoring.
- Agents that operate only within app boundaries can rely on host-level governance — their risk profile remains low.
In short, the more an agent thinks, decides, and acts on its own, the more it must be governed like a human employee.
Governance isn’t binary — it exists on a spectrum.
As agents evolve from simple in-app assistants to autonomous, decision-making entities, their need for visibility, controls, and auditability increases proportionally.
The following chart visualizes how autonomy (horizontal axis) and privilege (vertical axis) together determine the required level of governance.
Agents that sit higher and farther to the right on this spectrum must be treated as full digital identities, complete with ownership, monitoring, and policy enforcement.
Interpretation:
- Moving right → more autonomy and intelligence.
- Moving up → greater privilege and compliance risk.
- Agents in the top-right quadrant (Autonomous, Federated, Meta) require full identity lifecycle, continuous monitoring, and AI-specific guardrails.
As agents evolve in capability and scope, governance must evolve in parallel — from reactive oversight to proactive, identity-centric control.
The goal isn’t to slow innovation, but to ensure that every digital actor — whether human, machine, or AI — operates within a trusted, auditable framework.
Just as human identities follow a defined lifecycle — onboarding, provisioning, monitoring, and offboarding — agents require a parallel governance framework that reflects their autonomy, speed, and scope.
Traditional identity processes (like joiner–mover–leaver) were built for predictable human actors.
Agent ecosystems, however, are dynamic — they can be created by APIs, triggered by policies, and even managed by other agents.
This means governance must evolve from manual, periodic oversight to continuous, event-driven control.
The table below highlights how the classic identity governance lifecycle adapts to the agent era:
| Governance Function | Purpose | How It Evolves for Agents |
|---|---|---|
| Discovery | Identify all active digital actors — human, service, and agent. | Extend discovery beyond users to include agents running within SaaS, data, and AI environments. Requires scanning of APIs, connectors, and orchestration frameworks to detect hidden or external agents. |
| Registration | Establish an authoritative record of the agent’s identity. | Each agent must be registered with metadata such as owner, purpose, credential scope, and autonomy level. Registration can occur automatically at deployment or onboarding time. |
| Ownership & Sponsorship | Define accountability for every agent. | Every agent must have a human or team sponsor responsible for its actions, updates, and lifecycle. Ownership metadata ties all activity back to an accountable party. |
| Provisioning & Access Assignment | Grant minimal access required to perform the agent’s function. | Replace static API keys with policy-based credentials and dynamic scopes that adjust to context. This ensures least-privilege access for every agent. |
| Control who can invoke, configure, or delegate actions to agents. | Just as applications require user entitlements, agents require usage and invocation controls. Not every user should be able to trigger or manage high-privilege agents. Access to agents themselves must be governed, approved, and auditable, especially for autonomous, federated, or meta-agents. | |
| Monitoring & Behavioral Analytics | Detect anomalies, drift, or off-policy activity. | Move beyond access logs. Capture reasoning telemetry, decision paths, and cross-system interactions. Integrate with observability agents to create a “nervous system” for AI behavior. |
| Policy Enforcement | Apply rules and prevent conflicts of duty. | Extend Segregation of Duties (SoD) and Attribute-Based Access Control (ABAC) to include agent–agent and agent–system relationships. Define guardrails such as “an agent that remediates access cannot certify access.” |
| Decommissioning | Retire inactive or unowned agents safely. | Automate revocation when a project ends, an owner leaves, or an agent becomes orphaned. Deprovision credentials, archive logs, and remove external tokens or federated connections. |
Why This Matters
In a traditional model, governance happens after the fact — reviewing access once it’s already granted.
In an agentic model, governance must happen in real time, aligned with each agent’s creation, decision, and action.
This shift ensures that every autonomous entity — whether an AI policy enforcer, federated assistant, or swarm controller — operates within a clearly defined and continuously monitored identity lifecycle.
Governance is no longer a periodic control. It’s a living system — adapting at machine speed to manage machine intelligence.
As this new governance fabric takes shape, leaders need a simple way to determine which agents require identity-level oversight and which can remain under lightweight app governance.
The following decision matrix offers a clear, practical framework for that assessment.
Not every agent needs to be treated as a fully governed digital identity.
This decision matrix provides an at-a-glance guide for determining how much governance rigor each agent type demands based on its behavior, autonomy, and scope.
Governance should never be one-size-fits-all.
Instead, it must scale intelligently — matching the agent’s autonomy, access, and impact.
Agents confined to application sandboxes can remain within app-level controls.
But as soon as an agent acts independently, crosses systems, or holds credentials of its own, it steps into the realm of governed digital identities and must be treated with the same rigor as a human or service account. So I’ll leave you all with this:
The Rule of Thumb
An agent must have its own identity when it:
- Persists beyond one session, and/or
- Holds credentials or tokens, and/or
- Acts across multiple systems, and/or
- Makes decisions without a human prompt.
If none apply → app-level governance is enough.
If any apply → manage it as a governed digital identity.
AI agents are becoming digital coworkers — creating, reasoning, and acting across enterprise systems.
They deserve the same governance rigor as humans: ownership, monitoring, and accountability.
To stay compliant and trusted, organizations must evolve from identity governance to agent governance — treating every autonomous, persistent, or privileged AI as a first-class identity.
In the era of intelligent digital workforces, governance is no longer about who logs in — it’s about what acts, decides, and learns on your behalf.
