AI agent access is not controlled by one permission switch. A request succeeds only when the requester, agent identity, tool, credential, scope, target resource, downstream authorization, and runtime policy all line up. Human approval can pause a risky action, but it cannot repair an overpowered credential or replace the target service’s own authorization checks.
The authorization chain behind every agent action
Use this chain to analyze an agent instead of asking whether it is simply “allowed”:
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requester → agent identity → tool and action → credential and scope → downstream authorization → approval or policy gate → execution → audit record and revocation path
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This model is a practical synthesis of platform documentation, not a universal standard implemented identically by every product.
Identity: who is actually acting?
Start by naming the principal that a target service sees. It might be a signed-in user, an application or service principal, a managed identity, or a distinct governed agent identity. The choice determines accountability, consent, lifecycle ownership, and the blast radius of a mistake.
Delegated, user-context access
In a delegated design, the agent acts on behalf of a signed-in user. The downstream service can apply that user’s existing rights, and the user’s consent or an administrator’s consent grants the application specific delegated permissions. This is usually the right starting point when the agent handles user-owned mail, files, calendars, or messages. Microsoft describes delegated permissions as scopes represented in an access token’s scp claim; that token detail is Microsoft-specific, not a universal convention. See Microsoft’s agent access guidance.
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An autonomous agent runs without a user present and uses an application, service-principal, managed-identity, or agent-owned identity. This can support scheduled jobs and shared operational workflows, but the identity must have an explicit owner, a defined lifecycle, and a narrowly governed resource boundary. Microsoft documents app-only permissions as application permissions or app roles, represented in the token’s roles claim. Again, that claim naming is a Microsoft implementation detail.
Do not silently turn a user workflow into app-only access merely because it is easier to configure. App-only authority can combine access across many users or resources and may survive the departure of the person who originally connected the agent. Record who owns the identity, why it exists, where it may operate, and how it will be disabled.
Tools define possible actions, not final authority
A tool is an action interface: search, read, draft, write, send, delete, provision, or an API connector. An agent that has no tool cannot request that operation through that interface. But a tool allowlist does not automatically narrow the authority of the credential behind it.
For example, exposing only a “create ticket” tool does not make a broadly privileged service account safe if the connector can be altered, called with arbitrary parameters, or reused elsewhere. Enforce authorization in the connector and in the downstream service, not only in the agent’s visible menu.
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Separate actions whenever the target system permits it. Read, draft, edit, send, delete, and administrative operations should not share one undifferentiated tool when they have different consequences. Microsoft’s guidance states: “Each tool should have the smallest useful permission set.” The sentence appears in Microsoft Learn’s access patterns and controls for AI agents.
Scopes, roles, and resource boundaries
Scopes and roles are grants attached to an identity. They answer what the identity may do and, when the platform supports it, where it may do it. Effective access is the aggregate of direct grants, group membership, inherited roles, connector permissions, and permissions in downstream systems. Review that aggregate rather than inspecting one token or role in isolation.
| Access pattern | Typical principal | What to verify | Common risk |
|---|---|---|---|
| Delegated permissions | Signed-in user | Which user is represented, which scopes were consented to, and whether the resource honors that user’s rights | The agent can act with more of the user’s authority than its interface suggests |
| Application permissions or app roles | Autonomous application or service identity | Which app role is assigned, who approved it, and the exact resources covered | One workload identity accumulates broad cross-user access |
| Resource-level roles | User or workload identity | Whether access is limited to a file, mailbox, team, project, environment, or tenant segment | A broad directory role defeats a narrower intended boundary |
| Connector-specific grants | Agent-owned or user-owned connection | Which operations the connector exposes and how it passes identity downstream | Connector permissions and target-service permissions diverge |
Prefer the smallest useful resource and action boundary. Microsoft documents alternatives such as resource-scoped RBAC, Exchange RBAC for mailbox access, and Teams Resource-Specific Consent for a single team. Restricted permissions may require administrator consent, while per-team or per-resource consent can reduce exposure. Confirm the current tenant and product prerequisites before relying on any particular role or policy behavior.
Credentials: authentication is not authorization
A credential may be an OAuth token, API key, signed assertion, or managed identity. Presenting it authenticates the request; the target service then authorizes the requested operation. A credential can carry broad authority even when the agent has only a few visible tools.
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- Prefer managed identities or server-managed credentials where the platform supports them.
- Keep secret values out of prompts, model context, source code, logs, and user-visible tool results.
- Use short-lived tokens when practical, rotate credentials on a defined schedule, and test that old credentials stop working.
- Limit each credential to the resources and operations required by one workload or connector.
- Document an owner, expiration or rotation date, emergency disable procedure, and downstream systems that must be changed.
Google’s managed-agent credential design is a product-specific example: secret values are write-only, and an egress proxy resolves and injects credentials at request time rather than returning them to the endpoint. The details are documented at Google’s agent credentials guide; do not assume other platforms handle secrets the same way.
Downstream authorization remains the decision point
Every connector should pass an identity and an explicit operation to the target service, which must check the identity’s current rights against the requested resource. Do not treat a successful tool invocation as proof that the action was appropriate. A connector can be configured correctly while the target account has been granted an excessive directory role, inherited group membership, or an unrestricted API key.
Preserve a user’s authorization for user-owned information unless app-only access is an explicit, governed product decision. Where possible, make the target service enforce object-level or resource-level checks instead of trusting parameters supplied by the model or connector.
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Approvals are runtime gates, not permission boundaries
Use approval or policy checkpoints for consequential operations such as sending, posting, editing shared records, deleting data, submitting transactions, provisioning resources, or changing infrastructure. The gate should show the proposed operation, target, important parameters, identity, and expected effect so a reviewer can make an informed decision.
Approval behavior is platform-specific. OpenAI’s Workspace Agents documentation describes write actions for apps and connectors as “Always ask” by default in the described feature, while allowing other settings depending on the app. Check the current behavior at the Workspace Agents documentation.
An approval does not reduce the credential’s standing authority. If a reviewer approves a maliciously altered request, or if the same credential is exposed through another path, the downstream service still sees the broad grant. Keep least privilege in place, restrict which operations can reach approval, and avoid approval prompts for every harmless read if that would train users to click through them.
Network boundaries and returned data are separate controls
Action constraints can limit which calls an agent is allowed to request. Network controls can limit which destinations it can reach. Neither automatically solves the other problem.
OpenAI documents connector action constraints as restrictions on what an agent can ask a connector to do, while explicitly noting that they do not filter data returned through an otherwise permitted action. A read operation may therefore return more information than the action name implies unless the connector or target service applies a data boundary.
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Google’s managed-agent environment runs in an OS-isolated sandbox, but its overview says outbound network access is unrestricted by default unless an allowlist is configured. A sandbox limits process behavior; it is not automatically a network allowlist or a data-loss-prevention system. See Google’s managed-agents overview for the documented behavior and preview status.
Audit, lifecycle, and revocation
An audit record should let an investigator reconstruct the event. At minimum, capture:
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- the requester and effective principal;
- the tool, operation, and connector version;
- the target resource and relevant parameters;
- the authorization result and policy decision;
- the approval request, reviewer or policy identity, and timestamp;
- the downstream outcome, error, and returned-data classification.
Protect logs from tampering and avoid storing credentials or unnecessary sensitive payloads. Correlate agent-run identifiers with downstream service request IDs so an investigation can follow the action across systems.
Revocation must work beyond the agent interface. Disable the identity, revoke or expire tokens, remove role and group assignments, disable connector connections, and verify that a previously issued credential can no longer perform the action. Stopping new prompts while leaving an active app credential valid is not revocation. Microsoft highlights slow revocation and weak audit trails as agent risks in its least-privilege guidance.
How documented platform designs differ
Microsoft Entra and Microsoft 365 agent access
Microsoft’s model distinguishes interactive delegated access from autonomous application access and describes OAuth consent, access packages, resource-scoped RBAC, Exchange mailbox roles, and Teams Resource-Specific Consent. Its least-privilege guidance emphasizes agent identity, scope, tool access, aggregate permissions, auditing, and revocation. The cited setup also describes platform blocking of certain high-privilege Entra roles for agents; verify current tenant prerequisites before treating that as a guaranteed implementation rule. Start with Microsoft’s agent-access documentation.
OpenAI Workspace Agents and Codex
Workspace Agents supports end-user authentication and agent-owned connections. For agent-owned access, the documentation recommends a service account where possible and limiting it to what the agent needs. Action constraints govern requested connector operations, not necessarily returned data.
OpenAI’s Codex security article describes CLI and MCP OAuth credentials stored in the operating system’s secure keyring, workspace-bound access, and logs that can include requests, tool activity, approval decisions, tool results, and network-policy decisions. Those are controls described for that deployment context, not a promise about every coding-agent configuration. See OpenAI’s Codex security guidance.
Google Gemini API managed agents
Google describes managed agents as running in an OS-isolated sandbox. Its overview says outbound traffic is unrestricted by default and can be restricted or disabled with an allowlist. Google recommends trusted tools, minimum permissions, managed credentials, short-lived tokens, credential rotation, and output review for sensitive workflows. The overview labels managed agents Public Preview, so availability and behavior may change.
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A practical permission-design procedure
- Name the principal. Decide whether the agent acts for a signed-in user or as an autonomous workload. Assign an owner and document the business purpose.
- Inventory actions. List every tool operation, including reads, drafts, writes, sends, deletes, administrative changes, and background jobs. Separate operations with different consequences.
- Choose the narrowest resource boundary. Prefer a specific user, file, mailbox, team, project, environment, or tenant segment over an organization-wide grant.
- Map each action to a credential and grant. Record the scope, role, group membership, API permission, and downstream check that authorize it. Review effective aggregate access.
- Protect and age credentials. Store secrets in the platform’s managed mechanism, use short-lived credentials where practical, rotate them, and remove unused grants.
- Add runtime gates. Require policy or human review for consequential writes. Display the exact target and operation, not merely a generic “allow agent” prompt.
- Constrain network and data flow. Configure destination allowlists where available, and verify separately what data a permitted connector action can return.
- Instrument the path. Log principal, tool, resource, authorization, approval, outcome, and correlation IDs without leaking secrets.
- Exercise the kill path. Disable the agent, revoke tokens, remove assignments, and test a previously issued credential against the downstream service. Record the observed revocation time.
Common failure modes and fixes
“The tool list is small, so access is safe.”
Why it fails: the credential or connector may still have broad authority. Fix: inspect downstream roles and enforce per-operation checks at the target service.
“A human approves every write.”
Why it fails: approval does not narrow a credential, prevent an alternate access path, or guarantee that a reviewer understands a manipulated target. Fix: reduce standing privilege first, then use approval for selected high-impact actions.
“The agent acts for the employee.”
Why it fails: an app-only identity may actually be operating across users and persist after the employee leaves. Fix: verify the effective principal in the downstream audit log and choose delegated or app-only access deliberately.
“The sandbox blocks exfiltration.”
Why it fails: process isolation and network egress are different controls, and a permitted read can still return sensitive data. Fix: configure network allowlists where supported and enforce data filtering in the connector or resource service.
“Disabling the agent revokes access.”
Why it fails: tokens, service accounts, group assignments, or connector credentials may remain valid. Fix: perform and verify downstream revocation, including tests with already issued credentials.
Evaluation checklist for a real system
- Can you identify the effective principal for every run?
- Can the design preserve user-level authorization where user-owned data is involved?
- Are delegated scopes and app roles clearly distinguished?
- Is every tool split into the smallest useful read, write, send, delete, or administrative operation?
- Can each credential be limited to a defined resource boundary?
- Are secrets hidden from prompts, model context, source code, and ordinary logs?
- Are rotation, expiration, emergency disablement, and revocation tested?
- Which actions require approval, and what exactly does the reviewer approve?
- Do network restrictions limit destinations, and do data controls limit returned content separately?
- Can logs connect requester, identity, tool call, authorization result, approval, target, and outcome?
- Can the organization prove that disabling the agent removes downstream access?
Frequently Asked Questions
Are OAuth scopes, tools, and approvals interchangeable?
No. A scope or role grants authority to an identity, a tool exposes an operation surface, and an approval pauses a selected runtime action. Each control operates at a different layer.
Should an autonomous agent use a user’s credential?
Only when the product and governance model deliberately require user-context access. Otherwise use a separately owned workload identity with narrowly scoped permissions, an explicit owner, and a tested revocation process.
What is the first permission to remove when an agent is overprivileged?
Remove broad downstream roles and unused credential grants, then narrow each tool to the smallest resource and action boundary. Changing the prompt or hiding a tool does not reliably remove authority.
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