Delegated Flows

Q1: How do we model "agent attributes" for ABAC policies?

Model agent attributes as structured, verifiable claims that your policy engine can evaluate consistently, think principal + context + resource in every request.

What to include:

• Identity attributes: agent_id, fleet_id, tenant_id, owner/sponsor_id, agent_type (browser agent, API agent, workflow agent).

• Capability attributes: allowed tool set, max spend/impact tier, permitted data classes, allowed actions.

• Risk & posture: risk_score, device_posture, attestation_level, network_zone.

• Runtime context: environment=prod|staging, region, workload identity, session age, “step-up satisfied” flag.

How to make this ABAC-friendly:

• Keep attributes in a single policy input object and evaluate through a central PDP (Policy Decision Point) so services don’t implement policy logic ad-hoc. Engines like OPA are built exactly for this: you pass structured input, it returns an allow/deny decision.

• If you want formal analysis and safer policy authoring, consider policy languages designed for RBAC+ABAC combinations.

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Q2: How do we tie agent identity to runtime environment (prod vs. staging)?

Treat “environment” as a trust boundary, not a label.

Best-practice patterns:

• Separate trust domains per environment: Issue identities from distinct issuers/roots per env (prod ≠ staging).

• Workload identity attestation: Bind identity issuance to runtime attestation (Kubernetes node/workload, VM identity, etc.). SPIFFE/SPIRE is a common approach: it issues cryptographic workload identities after attestation.

• Environment-scoped identifiers: Use separate pools/namespaces per environment (e.g., different trust domain components). Google’s managed workload identity guidance explicitly recommends separate pools for dev/staging/prod.

Practical enforcement:

• Policy rule: deny prod access unless environment=prod and identity was minted from the prod trust domain.

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Q3: Can we query the identity graph efficiently for policy?

Yes, if you separate “relationship lookups” from “decision logic.”

Two proven ideas:

• Relationship-based authorization (ReBAC) / graph tuples: Store relationships like “principal has relation R to resource O” and evaluate quickly (the Zanzibar model). Zanzibar is designed for consistent, global, fast authorization checks at massive scale.

• Policy-as-code PDP + data fetch: Use a PDP (OPA/Cedar) for decisions, and query the graph as a Policy Information Point (PIP) input. OPA is explicitly built to decouple policy from apps and evaluate decisions from structured input + data.

Performance tips:

• Prefer tuple lookups (direct relationships) over deep traversal.

• Cache short-lived relationship results for hot paths.

• Keep “policy input” minimal and deterministic.

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Q4: Why is "intent" critical for authorizing AI agents?

Because with agents, “who” is not enough you must also authorize “what exactly is being attempted.”

“Intent” becomes a first-class authorization input because it:

• Prevents permission overreach: The agent may have broad access, but the specific action should be bounded to the user’s requested outcome.

• Enables least privilege per task: Authorize task-shaped access (e.g., “read invoices for vendor X” vs. “read all finance data”).

• Improves auditability: Logs become meaningful: intent → decision → tool calls → outcome.

Modern OAuth is moving in this direction:

• Rich Authorization Requests (RAR) lets you send fine-grained, structured authorization details (beyond coarse “scopes”), which maps well to “intent-shaped” permissions.

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Q5: What is "delegated authorization" for agents?

Delegated authorization is when a human (or system) allows an agent to act with limited authority on their behalf, without sharing credentials.

Standards-aligned ways to do it:

• OAuth delegated access: The agent receives tokens that represent the granted permissions.

• Token Exchange (OAuth STS): Exchange one token for another with different audience/scope/subject explicitly supporting delegation and impersonation semantics in the spec.

Key point: delegation should be audience-bound, scope-bound, and time-bound, with strong audit trails.

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Q6: How do we model "delegated" as a time-bounded object?

Treat delegation as its own grant record, not just “a token exists.”

A solid delegation object includes:

• delegation_id (immutable)

• sponsor_id (human authority)

• agent_id / fleet_id

• bounds: start_time, expires_at, max duration, renewal policy

• permissions: structured “authorization_details” (RAR-style), scopes, allowed resources, spending/impact limits

• constraints: environment, region, device/workload attestation level

• state: pending/active/suspended/revoked + reason + who/when

• revocation handles: token revocation + session kill hooks

Tokens become a projection of that object (short-lived, constrained).

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