AI agent governance: RBAC, MCP access, and delegation

AI agents have made “who has access?” a surprisingly incomplete security question.

Once software can choose tools, act across systems, delegate work, and keep running after the person who started the task has stepped away, authority becomes much more difficult to reason about. This guide gets into that messy middle: how to scope agent permissions, constrain delegation, govern MCP access, preserve accountability, and evaluate whether a platform like Plane gives security teams enough control once agents start doing real work.

Why AI agents need a different governance model

Organizations already govern non-human access through service accounts, workload identities, and API credentials. Agentic workloads add new considerations because they can choose tools at runtime, operate across systems, act without continuous human supervision, and delegate work when the architecture allows it.

AI agents and service accounts are different layers

A service account is a non-human identity used by an application or workload. An AI agent is software that may use a service account or another workload identity when accessing downstream systems.

The useful comparison is between a scripted workload and an agentic workload using machine credentials.

Production agents should also have a documented human or organizational owner responsible for their purpose, access scope, and lifecycle. Authorization should be reviewed or revoked when ownership changes or the agent's original purpose ends.

Different agent types require different controls

The Cloud Security Alliance's draft Agent Identity Governance Framework identifies five agent identity types, each with different credential, delegation, and lifecycle considerations:

• Copilot agents: Operate within an active user's session and can use session-bound access.
• Autonomous agents: Run without an active human session and require independently managed identities and credential lifecycles.
• Orchestrators: Coordinate other agents and need controls around what authority they can delegate.
• Ephemeral sub-agents: Exist for bounded tasks and should use short-lived credentials tied to the delegating agent.
• Agent-as-a-Service identities: Serve multiple tenants and require strong tenant isolation and separately scoped access contexts.

How RBAC should work for AI agents

For AI agents, RBAC works best as the baseline permission layer within a broader authorization model. Roles define what an identity can access and change, while agentic workloads introduce additional considerations around effective authority, tool access, delegation, resource sensitivity, and access duration.

Define roles around agent functions

Agent roles should align with the agent's documented purpose and include only the permissions required for that function.

A documentation agent may only need read access. An incident-management agent may also need permission to update selected fields or move work through defined workflow states.

Permission design should distinguish capabilities with different risk, especially:

• Read versus write access
• Workflow changes
• Destructive operations
• Administrative actions

Higher-risk capabilities can then receive narrower authorization or additional controls where appropriate.

Review effective authority across systems

An agent's effective authority spans the permissions and capabilities available across every connected system and tool.

For example, read access to an internal knowledge base, create access in a project management system, and messaging permissions may individually appear narrow. Combined, they could let the agent retrieve internal information, reproduce it elsewhere, and distribute it through another service.

Before production access, review the agent's connected systems, tools, and permissions together to understand the workflows those capabilities enable.

Where RBAC needs complementary controls

In practice, these controls are usually distributed across the authorization stack. Application-level RBAC establishes the baseline, while IAM, MCP clients or gateways, and authorization services can add controls around context, duration, delegation, and tool access.

Extend least privilege from scope to duration

Least privilege should account for both what an agent can access and how long that access remains available. For agentic workloads, reducing the duration of elevated access can limit exposure when credentials are compromised, an agent behaves unexpectedly, or the original business need changes.

Reduce standing access with time-bound privileges

Standing privileges remain available for as long as the authorization is valid, including between tasks that actually require them.

Just-in-time access narrows that window by granting elevated privileges only when needed. Depending on the infrastructure, access can expire:

• When a task completes
• At the end of a session
• After a predefined time limit

The grant can also be restricted to specific resources and operations.

For agent deployments, keep baseline permissions narrow and make higher-risk privileges available only for the approved access window. NIST's Zero Trust guidance describes this broader principle as providing just enough privilege when it is needed and removing it afterward.

Use approval gates for high-impact actions

Some requests carry enough risk to require additional authorization or outright denial.

Examples include:

• Requesting access beyond the agent's approved scope
• Accessing data above its authorized sensitivity level
• Performing destructive changes or sensitive data exports
• Creating or modifying identities or credentials for other agents
• Persisting data outside an approved boundary across tasks

A practical authorization flow can use three outcomes:

1. Proceed automatically: The request falls within the agent's approved scope and satisfies policy.
2. Require approval: The request is permitted but crosses a defined risk threshold.
3. Deny: The request falls outside the authority the organization is willing to grant.

This keeps human review focused on actions that require additional judgment or accountability while routine work continues under automated authorization.

How to govern delegated authority

Delegation changes the authorization path because an agent may act for a human principal or pass part of its authority to another agent. Governance needs to preserve who authorized the action, which agent acted, and what authority was actually delegated.

Govern human-to-agent delegation

When an agent acts on behalf of a user, its authority should be bounded by both what the user is allowed to do and what the agent is authorized to do.

A risky setup looks like this:

1. An administrator configures an agent using highly privileged credentials.
2. A lower-privileged user invokes that agent.
3. The system evaluates the agent's credentials but does not re-check the user's authority.
4. The agent can now perform actions the user could not perform directly.

To avoid this, delegated authorization should preserve two identities where the infrastructure supports it:

• Human principal: the user whose authority initiated the request
• Acting agent: the agent that actually performed the action

The resulting authorization should also be limited to the resources and operations required for that delegated task.

Downstream systems can then answer two important questions: Who authorized this action, and which agent performed it?

Constrain agent-to-agent delegation

When an orchestrator delegates work to a sub-agent, the delegated authority should stay within the permissions the orchestrator is itself authorized to grant. A delegation chain can preserve or narrow scope, but should not expand it.

For each delegation, retain enough context to reconstruct:

• The delegating identity
• The receiving agent
• The authority that was passed
• The task or purpose of the delegation
• The applicable access window

For ephemeral sub-agents, credentials should also have a fixed expiry and preserve their relationship to the delegating agent.

This creates a traceable chain from the originating principal through the agents that ultimately performed the action.

Prevent the confused deputy problem

A confused deputy problem occurs when a component with legitimate authority is induced to use that authority for a caller or purpose that was not properly authorized. Agentic systems create several paths for this to happen.

For example:

• A lower-privileged user invokes a shared agent that operates with broader credentials, and the system fails to re-evaluate whether that user is authorized for the requested action.
• Malicious instructions embedded in an email, document, website, or other retrieved content influence an agent to invoke its legitimate tools or permissions for an unauthorized purpose.

Mitigating this risk requires authorization at the point where consequential actions are executed. Keep human and agent identities distinguishable, limit delegated scope, validate the caller's authority, and preserve delegation context so downstream systems can evaluate and attribute the request correctly.

What an AI agent audit trail should establish?

For consequential agent actions, audit records should make it possible to reconstruct who or what acted, under what authority, against which resource, and with what outcome. Agentic workflows may also require delegation, approval, and runtime context where those factors affect authorization or investigation.

What should be captured for agent actions

Runtime details such as agent or model version can help establish provenance during an investigation. They identify the configuration involved, but do not explain why a model produced a particular decision.

Generate authoritative evidence outside the agent

Agent-generated explanations and self-reported histories can support debugging, but governance records need stronger integrity guarantees.

Security-sensitive events should be captured by trusted components involved in authorization or execution, such as the authorization service, tool gateway, API boundary, downstream resource, or application platform. The agent should not be able to modify or delete the authoritative record.

Auditability and observability serve different purposes

Both matter in agent deployments. Observability explains how a workflow behaved, while audit records establish who or what acted and under which authority.

Protect audit-record integrity

Audit records used for governance should:

• Be protected from unauthorized modification or deletion
• Be managed by appropriately authorized identities
• Capture security-relevant failed or denied actions where needed for investigation

Higher-assurance environments can add controls such as append-only storage, write-once retention, or cryptographic integrity protection.

Govern tool and MCP access separately

System access and tool access need their own controls. An agent that only needs to retrieve and summarize information should have access to the required read operations, without receiving write, delete, export, or administrative capabilities by default.

Control tool exposure and execution

Two control points matter:

• Tool exposure: Limit the tools presented to the agent to those required for its purpose. The MCP protocol allows a server to vary the advertised tool set based on the authorization presented, though many servers advertise a fixed set and rely on execution-time authorization instead.
• Tool execution: Authorize each operation when it is invoked. The downstream service or trusted authorization layer should verify whether the caller can perform that action on the requested resource.

Reducing the available tool set limits the capabilities the model can select, while execution-time authorization provides the enforcement boundary for each attempted action.

Preserve authorization boundaries across services

MCP access tokens should be issued for their intended resource and validated by that resource. When an MCP server calls another protected service, it should use credentials appropriate to that downstream service rather than forwarding a token issued for a different resource.

For delegated workflows, the authorization architecture should also retain enough trusted context to reconstruct who initiated the request, which agent acted, and how authority reached it.

How Plane MCP applies these controls

Plane's MCP server exposes Plane API capabilities as tools that MCP-compatible AI clients can call.

• For Plane Cloud, interactive clients can connect over HTTP using OAuth. Automated workflows can use HTTP with a Plane API token, while local stdio mode suits local development and single-user setups. Teams can also deploy their own Plane MCP server against a self-hosted Plane instance using HTTP with OAuth or local stdio.
• The authorization boundary follows the credential used for the connection. With OAuth, the client acts through the authenticated Plane user and their existing Plane access. Token-based connections use the access associated with the supplied Plane credential. Plane's API continues to enforce authorization when MCP tools perform reads or writes.

Plane's MCP server advertises the same tool set to every authenticated client; it does not vary by the caller's Plane role. Authorization is enforced when a tool executes, so a user without permission to delete a work item cannot delete one through MCP even though the capability appears in the tool list. Narrowing the exposed tool set is handled in the MCP client or gateway layer.

Tool availability and agent behavior therefore still need controls outside Plane. Teams should decide which tools their AI client can use, when writes require confirmation, and what additional delegation or authorization controls the surrounding agent infrastructure requires.

What security teams should verify before granting production access

Before an agent receives production access, security teams should be able to verify its identity, authority, and accountability across the systems it can reach.

Use this as a stack-level checklist across the application, IAM or IdP, MCP client or gateway, and authorization infrastructure. Individual platforms will typically provide only part of the enforcement and evidence described below.

Identity and ownership

• The agent has an identifiable execution identity that is not shared with unrelated agents, workloads, or users.
• A human or organizational owner is documented for its purpose, access scope, lifecycle, and governance oversight.
• Its business purpose is specific enough to evaluate whether the requested capabilities are necessary.
• Credential issuance, expiry, rotation, and revocation processes are defined for the identity type in use.

Permissions and access scope

• Permissions match the agent's documented function and required operations.
• Read, write, destructive, and administrative capabilities can be controlled separately where their risk differs.
• The agent's effective authority has been reviewed across its connected systems and tools.
• Elevated access is time-bound where continuous access is unnecessary.

Delegation and tool access

• The tools exposed to the agent are limited to those required for its purpose, with authorization enforced when tools are invoked.
• Any agent-to-agent delegation has defined limits on scope, recipient, purpose, and duration.
• Delegated authority cannot exceed what the delegating identity is authorized to grant.
• Policy defines which high-impact actions can proceed automatically, require additional approval, or must be denied.

Audit and revocation

• Security-relevant successful, failed, and denied actions are logged according to the organization's audit policy.
• Records identify the acting agent and preserve relevant authorization, delegation, and originating-principal context where applicable.
• Authoritative audit records are protected from alteration or deletion by the agent being monitored.
• Revocation is tested so that agent credentials and relevant downstream access can be withdrawn when required.

Govern agents throughout their lifecycle

An agent's risk profile can change after deployment as its purpose, permissions, integrations, ownership, or level of autonomy changes. Lifecycle governance keeps those changes visible and gives teams a defined process for review and retirement.

Maintain an agent inventory

For each production agent, record at least:

• Accountable owner and owning team
• Documented business purpose
• Agent type or operating model
• Authorized systems and tools
• Current permission scope
• Credential type, expiry or rotation requirements, and revocation path
• Delegation capabilities
• Last review and next review date

The inventory should stay current as the agent or its access changes.

Reassess when the agent changes

Trigger a review when changes could alter the agent's authority or risk, including:

• Its purpose or workflow scope changes
• New tools, APIs, or systems are connected
• Permissions or delegation capabilities expand
• Its level of autonomy increases
• Its owner or owning team changes
• The sensitivity of the data it accesses changes

Periodic access reviews can complement these event-driven checks, with frequency based on organizational risk.

Decommission retired agents completely

Retirement should cover the agent and the access created for it:

• Revoke associated credentials and tokens
• Remove permissions and downstream access grants
• Disable or remove agent-specific integrations
• Update the inventory to record the retirement
• Preserve required audit records and related artifacts according to applicable retention policies

Runtime shutdown and access revocation should both be verified so retired agents do not retain usable credentials or permissions.

What agent governance looks like inside project management systems

Project management platforms can contain operational records that determine who owns work, how it progresses, and which workflows or integrations run next. When agents can modify those records, their actions need the same authorization and accountability expected for other consequential changes.

These controls give buyers a practical way to evaluate how a project management platform handles agent-assisted work: what can the agent reach, what can it change, which actions require additional control, and what evidence remains afterward?

How Plane supports governed agent access and accountability

For teams evaluating agent-assisted work in Plane, four questions matter: who is acting, what can they change, where human control remains, and what evidence is left afterward.

Know which identity is acting

Two identity patterns are particularly relevant to agent governance in Plane.

Native Plane Agents, currently in beta, operate as non-billable bot users and leave their activity in the work-item timeline. They currently have workspace admin access, and custom roles or GAC permission schemes do not apply to them.

For MCP-connected activity, Plane attributes actions to the identity used for the connection. Cloud MCP uses the signed-in Plane user, while token-based MCP uses the API token. Plane does not expose a separate agent identity for these actions, so teams that need per-agent attribution should preserve that context in their MCP client or gateway.

Keep agent actions inside existing access boundaries

Plane governs access through workspace, project, and Teamspace roles, with custom roles and permission schemes available on Enterprise Grid. Plane does not currently provide roles defined specifically around an agent’s function.

MCP-connected clients use the permissions of the identity used for the connection and do not receive a separate privilege set. Where narrower MCP access is needed, teams can use a dedicated Plane user, assign that user the appropriate role or custom permission scheme, and connect through that user’s token.

Put policy around sensitive workflow changes

Sensitive workflow changes can carry controls beyond the underlying edit permission.

Workflows in Plane define which state transitions are allowed. On Enterprise Grid, approval flows can hold a transition until an authorized approver accepts or rejects it, while transition conditions can run Plane Runner pre-validation before the transition proceeds.

These controls are useful when agent-assisted work reaches sensitive points in a process, such as an approval stage or another transition where the organization wants an enforceable check before work advances.

Preserve evidence after the action

Plane separates work-item activity from its audit logs. Native agent edits appear in the relevant work-item activity history, which is separate from the tamper-evident audit trail.

• On Enterprise Grid, Plane provides both Workspace Audit Logs and Project Audit Logs. Workspace Audit Logs capture supported workspace governance events such as membership and role changes, workspace settings, API token creation and revocation, webhook creation and deletion, and audit log searches and exports.
• Project Audit Logs provide a project-scoped view of the same underlying audit records, focused on project configuration, membership, and permissions. API token and webhook events are recorded in Workspace Audit Logs, while sign-in events are recorded at the instance level. None of these event types appear in Project Audit Logs.
• Plane’s Audit logs are append-only. Administrators cannot edit or delete entries after creation, and records are cryptographically chained so their integrity can be verified through the API.

Teams that need per-agent attribution, detailed tool calls, delegation context, or runtime provenance should capture that evidence in their MCP client, gateway, or surrounding authorization infrastructure.

Connect Plane to existing identity governance

Plane supports SAML 2.0 and OIDC SSO, while Enterprise Grid adds directory-oriented identity controls including LDAP and SCIM. IdP Group Sync is also an Enterprise Grid capability and is currently available on self-hosted Commercial and Airgapped Editions.

For OAuth-based hosted MCP connections, Plane applies the connected user's existing access when the client acts in the workspace. This lets enterprise identity controls govern the Plane account lifecycle while Plane's authorization model governs what that identity can do inside the platform.

Closing thoughts

Agent governance becomes more important as AI systems move from assisting with work to acting inside the tools where that work is managed. Security teams need clear answers around identity, permission scope, delegation, workflow controls, and the evidence available after an action takes place.

Within the broader governance stack, Plane enforces the identity's existing permissions, applies workflow rules and approvals to supported work-item transitions, and provides governance audit logs for supported Plane events. Controls such as JIT access, time-bound elevation, agent-to-agent delegation, and per-agent MCP attribution remain in the surrounding IAM, MCP client or gateway, and authorization infrastructure.

If your organization is evaluating how AI agents can operate inside project and product workflows without weakening existing access controls, talk to sales. See how Plane handles permissions, approvals, MCP-connected access, and auditability, and evaluate how it fits into your existing security architecture.