Identity and platform engineers designing delegated access for autonomous workflows.
Whether Endram provides the runtime control and evidence needed for ai agent authorization.
The control gap
Authentication proves which workload holds a credential. It does not decide whether this agent, acting for this user, may perform this action on this resource now.
Authorization starts after identity is verified. The decision input should distinguish the stable agent principal, runtime instance, delegating user or service, task, purpose, environment, canonical action, target resource, arguments relevant to risk, and an idempotency key. Caller-provided names and free-form model rationales can be retained as context but should not satisfy a rule intended for cryptographic provenance.
Evaluate the response as an enforceable contract. An allow or deny needs a policy version, reason, expiry, and request binding. A pending result must pause before the provider call. An approved result should not authorize altered arguments or unlimited replay. Endram signs decision receipts and can issue one-use grants for supported adapters while allowing other callers to report their execution outcome through the API.
What good looks like
Authorization is evaluated per tool call using both machine identity and delegated human authority.
- Agent and user as separate principals
- Action and resource-level policy
- Contextual constraints and expiry
- Default-deny enforcement
A production workflow
- Authenticate the agent
- Attach delegated-user context
- Evaluate action and resource
- Issue a decision and signed receipt
Evidence to require
- Principal chain
- Matched attributes and rule
- Decision reason
- Execution outcome
Buyer checklist
- Can the product enforce a decision before the external tool executes?
- Can policy distinguish the agent, delegated user, tool, resource, and environment?
- Can reviewers see the exact requested action and approve it without broadening future access?
- Does every allow, deny, and approval retain the policy version and reason?
Practical answers
Common implementation questions
What does Endram control for ai agent authorization?
Endram evaluates the concrete tool call at runtime. It can allow, deny, or pause the call for approval using agent identity, delegated authority, action, resource, environment, and request context.
Does Endram replace the tool's own IAM?
No. Keep native IAM and OAuth scopes as the outer boundary. Endram adds a decision layer for the actions an agent attempts inside those credentials.
Can teams evaluate policies before enforcing them?
Yes. Shadow mode records the decision Endram would make without interrupting the call, so teams can measure impact before switching a policy to enforcement.