Authorization for autonomous systems

Policy-based access control for the age of AI agents.

Agentic systems make dynamic decisions, call tools, and move across data boundaries in real time. Policy-based access control (PBAC) keeps those actions safe by evaluating the full context of every request instead of relying on static roles alone.

Explore PBAC See a sample policy
Context-aware decisions PBAC evaluates attributes such as actor, action, resource sensitivity, environment, and declared purpose before allowing an operation.
Externalized rules Policies live outside application code, so teams can update authorization logic without rewriting every workflow or tool integration.
Consistent enforcement The same policy engine can govern web apps, APIs, background jobs, and AI agents across your stack.

Why static authorization breaks for agents

AI agents are not limited to one predictable screen or endpoint. They can chain actions, retrieve private data, execute tools, and adapt based on prompts or intermediate results.

  • Roles rarely capture the intent behind an agent's current task.
  • One agent session may touch resources with very different sensitivity levels.
  • Approvals often need to reflect time, location, user delegation, or risk signals.

How PBAC solves agentic authorization

PBAC lets you express rich rules such as an agent may summarize a document but may not export it outside the tenant or tool execution requires elevated approval when the request exceeds the user's delegated scope.

  • Evaluate runtime context for every agent decision.
  • Separate user permissions from agent execution safeguards.
  • Create explainable allow/deny decisions with audit trails.

Decision flow for agentic authorization

1. Collect context

Capture who initiated the request, which agent is acting, which tool is being used, what resource is targeted, and the current environmental signals.

2. Evaluate policy

Ask a policy engine whether the requested action is permitted under the current context, obligations, and risk thresholds.

3. Enforce and audit

Allow, deny, redact, or require additional approval while recording the decision for observability and compliance.

Sample PBAC policy

allow if
  actor.type in ["user", "agent"]
  and actor.tenant == resource.tenant
  and action in ["read", "summarize"]
  and purpose == "support_case_resolution"
  and resource.classification != "restricted"
  and agent.tool in ["knowledge-search", "ticket-summary"]
  and risk.score < 40

require_approval if
  actor.type == "agent"
  and action == "export"
  and resource.classification in ["confidential", "restricted"]

What good PBAC looks like

  • Policies are expressive enough to model intent, delegation, and tool constraints.
  • Applications ask for decisions instead of embedding business rules everywhere.
  • Teams can explain exactly why an agent action was allowed or denied.
  • Enforcement scales as new agents and tools are added.

PBAC gives agentic systems the guardrails they need.

If your applications are evolving from static interfaces to autonomous workflows, authorization must evolve too. PBAC provides the flexible, contextual, and auditable control plane needed to keep AI agents aligned with user permissions and organizational policy.