Identity Modeling

Q1. How do we represent an agent identity in our directory?

An agent identity should be represented as a first-class, non-human principal in your directory, not as a user or generic service account. It should have its own immutable identifier, metadata, and lifecycle state.

• Store agent attributes such as purpose, owner, tenant context, and allowed capabilities.

• Bind the agent to policies rather than embedding permissions in code.

• Use tags or attributes to capture intent and operational scope.

• Ensure the agent identity is auditable independently of humans.

• Support enable, suspend, and retire states.

• This makes agent behavior governable and explainable.

• Directories become control planes, not just lookup tables.

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Q2. Should agents have unique IDs per task or per instance?

The best practice is unique identity per logical agent, not per task invocation. Tasks are executions; agents are actors.

• Use a stable agent ID for lifecycle governance.

• Track individual executions via session or execution IDs. This avoids identity sprawl while preserving traceability.

• Per-task IDs create audit noise and management overhead.

• Per-instance IDs may be useful for ephemeral agents.

• Choose based on agent lifespan and autonomy.

• Govern identities, not every function call.

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Q3. How do we model "agent groups" or "agent fleets"?

Agent groups (or fleets) should be modeled as logical collections of agent identities governed by shared policies.

• Groups simplify permission management without cloning roles.

• Policies apply to the group, not each agent individually.

• Agents can join or leave groups dynamically.

• Group membership should be attribute-driven, not manual. This mirrors modern workload identity patterns.

• Auditing tracks both group policy and agent behavior.

• Fleets scale governance without losing control.

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Q4. How do we model "tenant-specific agents" in multi-tenant SaaS?

In a multi-tenant SaaS environment, tenant-specific agents should be modeled with a strict tenant boundary embedded directly into their identity. Each agent identity must carry a unique tenant ID, which becomes a first-class attribute used during authorization decisions.

Policies should consistently enforce tenant isolation at runtime, ensuring agents can operate only within their assigned tenant context and are technically incapable of accessing resources outside it.

Agents should be provisioned automatically as part of tenant onboarding and deprovisioned cleanly during tenant offboarding to prevent orphaned access. Shared credentials across tenants must be avoided entirely, as they break isolation guarantees. This approach ensures strong tenant isolation, regulatory compliance, and predictable, auditable agent behavior at scale.

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Q5. How do we model "agent roles" without role explosion?

To model agent roles without causing role explosion, avoid creating large numbers of static roles and instead combine a small, reusable set of capability-based roles with contextual attributes. Define broad roles that represent what an agent can do, and use policy conditions to dynamically narrow their scope at runtime.

Attributes such as task type, data classification, or execution environment refine access without requiring new roles. This keeps roles stable and easy to manage while policies remain flexible and adaptive. The result mirrors ABAC-style governance for agents, allowing control to scale without added complexity.

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Q6. How do we model shared agents used across many customers?

Shared agents used across many customers should be modeled with a single core identity combined with explicit, per-request tenant scoping. Every action must carry tenant context as a required input, and authorization should always evaluate both the agent’s identity and the provided tenant scope—never relying on implicit assumptions.

Shared agents must operate in a stateless manner across tenants, ensuring no context or privileges leak between executions. All actions should be logged with clear tenant attribution to support auditing and compliance. This approach prevents privilege bleed and audit gaps—because shared never means unscoped.

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Q7. How do we prevent cross-tenant leakage through shared agents?

Cross-tenant leakage is prevented by enforcing tenant boundaries at the identity and policy layer. Every request must include explicit tenant context, with policies rejecting any action without tenant binding.

Data access is always tenant-scoped, audit logs capture tenant, agent, and action together, and no secrets are shared across tenants—making identity the primary enforcement point.

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Q8. Can we represent nested orgs and hierarchies for agents?

Yes, nested organizations and hierarchies can be represented for agents by modeling agent identities with the same hierarchical org and tenant structures used for users. Agents inherit baseline constraints from parent organizations, while sub-organizations can enforce stricter policies as needed.

Authorization evaluates the full hierarchy during access decisions, enabling accurate enforcement, delegated administration, and audit trails that reflect organizational context. This approach allows Agentic IAM to scale with real-world enterprise and B2B SaaS complexity.

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