Cedar Policy Engine: Governance-First AI Agent Authorization

Date: 2026-03-19 Author: Deep Research Agent (Moklabs) Tags: cedar, authorization, policy-engine, agent-governance, abac, rbac Products: OctantOS, Paperclip, AgentScope Enables: MOKA-267 (Integrate Cedar policy engine for governance-first agent authorization)

Executive Summary

Cedar is AWS’s open-source authorization policy language, purpose-built for expressive, fast, safe, and formally verifiable access control. In March 2026, Cedar became the backbone of Amazon Bedrock AgentCore Policy — the first production-grade, deterministic authorization layer for AI agent-to-tool interactions. Cedar joined the CNCF as a Sandbox project, signaling vendor-neutral cloud-native adoption.

Why this matters for OctantOS: OctantOS needs a governance-first approach to agent authorization — controlling what agents can do, on which resources, under what conditions. Cedar provides exactly this: a deterministic policy engine that evaluates every tool invocation at runtime, decoupled from agent code, with formal safety guarantees no LLM-based guardrail can match.

Key recommendation: Adopt Cedar as OctantOS’s authorization engine for agent governance, using the self-hosted @cedar-policy/cedar-wasm npm package (no AWS dependency required). Model agents as principals, tools as actions, and resources as Cedar entities with ABAC conditions for budget, risk level, and autonomy constraints.

1. Cedar Architecture Overview

1.1 Core Concepts

Cedar’s authorization model is built on four primitives:

Concept	Description	OctantOS Mapping
Principal	Who is requesting access	Agent (by ID, role, tier)
Action	What operation is requested	Tool invocation, API call, resource mutation
Resource	What is being acted upon	Mission, task, service, budget pool
Context	Runtime attributes of the request	Risk level, cost estimate, autonomy level

1.2 Evaluation Semantics

Cedar’s evaluation follows strict, deterministic rules:

Default Deny — If no policy matches, the request is DENIED
Forbid Overrides Permit — Any matching forbid policy results in DENY, regardless of permit policies
Order Independence — Policy evaluation order does not affect the result
Deterministic Termination — Always terminates, always produces the same result for the same input

This is fundamentally different from LLM-based guardrails, which are probabilistic. Cedar policies are provable — the engine uses SMT solvers to mathematically verify policy properties.

1.3 Policy Language Syntax

// Permit agents in the "engineer" role to execute build tools
permit(
  principal in Role::"engineer",
  action in [Action::"build", Action::"test", Action::"lint"],
  resource
);

// Forbid any agent from deploying to production without senior approval
forbid(
  principal,
  action == Action::"deploy_production",
  resource
) unless {
  context.has("approval") && context.approval.level == "senior"
};

// Budget-constrained permission
permit(
  principal is Agent,
  action == Action::"invoke_api",
  resource
) when {
  context.estimated_cost < principal.budget_remaining &&
  principal.autonomy_level >= resource.required_autonomy
};

2. Authorization Patterns for AI Agents

2.1 Pattern 1: Role-Based Agent Control (RBAC)

Map agent tiers to Cedar groups. Permissions flow from group membership.

// Tier 1: Strategic agents (CEO, CTO) — full mission control
permit(
  principal in AgentTier::"strategic",
  action in ActionGroup::"mission_lifecycle",
  resource is Mission
);

// Tier 2: Lead agents — can create tasks, not missions
permit(
  principal in AgentTier::"lead",
  action in [Action::"create_task", Action::"assign_task", Action::"review_task"],
  resource is Task
);

// Tier 3: Engineer agents — execute assigned tasks only
permit(
  principal in AgentTier::"engineer",
  action in ActionGroup::"task_execution",
  resource is Task
) when {
  principal in resource.assignees
};

2.2 Pattern 2: Attribute-Based Governance (ABAC)

Use runtime attributes for fine-grained control — budget limits, risk levels, autonomy levels.

// Only allow high-cost operations for agents with sufficient budget
permit(
  principal is Agent,
  action == Action::"provision_infrastructure",
  resource is Service
) when {
  context.estimated_cost <= principal.monthly_budget_remaining &&
  resource.risk_level != "critical"
};

// Enforce risk-based escalation
forbid(
  principal is Agent,
  action in ActionGroup::"destructive_operations",
  resource
) when {
  resource.risk_level == "critical" &&
  principal.autonomy_level < 4
};

2.3 Pattern 3: Tool Access Control (AgentCore-Style)

Model tool invocations as Cedar actions, following the AWS Bedrock AgentCore pattern.

// Agent can invoke search tools freely
permit(
  principal is Agent,
  action == Action::"SearchTool__web_search",
  resource == Gateway::"octantos-gateway"
);

// Agent can invoke write tools only within project scope
permit(
  principal is Agent,
  action == Action::"GitTool__commit",
  resource == Gateway::"octantos-gateway"
) when {
  context.input has repository &&
  context.input.repository in principal.allowed_repositories
};

// Block all database mutations outside maintenance windows
forbid(
  principal,
  action in ActionGroup::"database_mutations",
  resource
) unless {
  context.maintenance_window == true
};

2.4 Pattern 4: Policy-Aware Agent Loop

Based on the OpenClaw + Cedar pattern (Windley, Feb 2026):

Agent proposes action
    -> PEP intercepts
    -> Cedar PDP evaluates policies
    -> ALLOW: tool executes normally
    -> DENY: structured denial returned to agent
        (includes reason + hints for alternative actions)

Critical insight: The agent contains NO authorization logic. The denial is returned as a structured result the agent can reason about and adapt to. This maintains agent independence while enforcing deterministic boundaries.

3. Cedar vs Alternatives

3.1 Cedar vs OPA/Rego

Dimension	Cedar	OPA/Rego
Performance	42-60x faster than Rego	Slower, scaling challenges
Safety	Formally verified, deterministic	Runtime exceptions possible
Learning curve	Hours	30-40 hours
Expressiveness	Domain-specific (authorization)	General-purpose
Analyzability	SMT-backed formal proofs	No formal verification
Ecosystem	CNCF Sandbox, AWS native	CNCF Graduated, broad adoption
AI agent support	Bedrock AgentCore native	Community integrations
Best for	Application-level authz	Infrastructure-level authz

Verdict for OctantOS: Cedar wins. Application-level agent authorization is Cedar’s exact sweet spot. The 42-60x performance advantage matters for per-tool-invocation evaluation. Formal verification provides compliance guarantees that OPA cannot.

3.2 Cedar vs OpenFGA/Zanzibar

Dimension	Cedar	OpenFGA
Model	RBAC + ABAC + ReBAC	Pure ReBAC (Zanzibar)
Policy language	Cedar (declarative)	JSON tuples
ABAC support	Native `when` conditions	Limited
Self-hosted	WASM, no external deps	Requires API server
AI agent patterns	AgentCore reference arch	None

Verdict: Cedar. OpenFGA is excellent for relationship-heavy models (Google Drive-style sharing) but lacks ABAC depth needed for agent governance with budget/risk/autonomy attributes.

4. Implementation Architecture for OctantOS

4.1 Recommended Architecture

                    +------------------+
                    |   Agent Runtime  |
                    |  (OctantOS Core) |
                    +--------+---------+
                             |
                    Tool invocation request
                             |
                    +--------v---------+
                    | Policy Enforcement|
                    |   Point (PEP)    |
                    | (Fastify middleware)|
                    +--------+---------+
                             |
              Build Cedar AuthzRequest
              (principal, action, resource, context)
                             |
                    +--------v---------+
                    |   Cedar PDP      |
                    | (@cedar-policy/  |
                    |  cedar-wasm)     |
                    +--------+---------+
                             |
                    ALLOW or DENY + reason
                             |
                    +--------v---------+
                    |  Execute or deny |
                    |  with feedback   |
                    +------------------+

4.2 Tech Stack Integration

Component	Technology	Package
Policy Engine	Cedar WASM	`@cedar-policy/cedar-wasm`
Policy Store	PostgreSQL (existing)	Policies as rows in `cedar_policies` table
PEP (API)	Fastify middleware	Custom middleware
PEP (Agent loop)	Hook in agent execution	Custom interceptor
Schema	Cedar schema JSON	Version-controlled in repo
Analysis	Cedar CLI	`cedar-policy/cedar` (Rust CLI)

4.3 NPM Packages

# Core authorization engine (WASM, no external deps)
npm i @cedar-policy/cedar-wasm

# Optional: Express/Fastify middleware helper
npm i @cedar-policy/cedar-authorization

Key detail: @cedar-policy/cedar-wasm runs Cedar entirely in-process via WebAssembly. No external service required. Sub-millisecond evaluation. Three subpackages:

@cedar-policy/cedar-wasm — ES modules (default)
@cedar-policy/cedar-wasm/nodejs — CommonJS for Node.js
@cedar-policy/cedar-wasm/web — Browser

4.4 Entity Model for OctantOS

// Schema definition (cedarschema)
namespace OctantOS {
  entity Agent in [AgentTier, Team] {
    autonomy_level: Long,
    monthly_budget_cents: Long,
    budget_spent_cents: Long,
    capabilities: Set<String>,
    status: String,
  };

  entity AgentTier in [AgentTier] {};
  entity Team {};

  entity Mission {
    risk_level: String,   // "low" | "medium" | "high" | "critical"
    status: String,
    owner: Agent,
    budget_cents: Long,
  };

  entity Task {
    mission: Mission,
    assignees: Set<Agent>,
    required_autonomy: Long,
    status: String,
  };

  entity Tool {
    category: String,     // "read" | "write" | "deploy" | "admin"
    risk_level: String,
    cost_per_invocation: Long,
  };

  entity Gateway {};

  action invoke_tool appliesTo {
    principal: Agent,
    resource: Tool,
    context: {
      estimated_cost: Long,
      mission_id: String,
      task_id: String,
      environment: String,
    }
  };

  action create_mission appliesTo {
    principal: Agent,
    resource: Gateway,
  };

  action assign_task appliesTo {
    principal: Agent,
    resource: Task,
  };

  // ... more actions
}

4.5 Progressive Enforcement Strategy

Following the AgentCore “Record -> Enforce” pattern:

Phase	Mode	Behavior
Phase 1	Record	Log all policy decisions, never block. Establish baseline.
Phase 2	Shadow	Evaluate policies, log would-be denials, but allow all. Validate rules.
Phase 3	Enforce	Actively block unauthorized actions. Production mode.

This allows safe rollout without disrupting running agents.

5. Policy Governance & Compliance

5.1 Formal Verification

Cedar’s killer feature for compliance: automated reasoning proves properties about your policies.

# Verify: "No engineer agent can ever deploy to production"
cedar analysis --policy-set ./policies/ \
  --property "forbid(principal in AgentTier::engineer, action == deploy_production, resource)"

If the property holds, Cedar returns a mathematical proof. If not, it returns a concrete counterexample showing which policy combination allows the violation.

5.2 Regulatory Alignment

Regulation	Cedar Capability
EU AI Act	Deterministic, auditable authorization decisions
ISO 42001	Policy-as-code with version control
NIST AI RMF	Formal verification of safety properties
SOC 2	Complete audit trail of all authorization decisions

5.3 Policy Management Best Practices

Version-control policies alongside code (Git)
Schema-first development — define entity model before writing policies
Policy testing — Cedar CLI supports policy validation and testing against sample requests
Separation of concerns — forbid policies for security team, permit policies for engineering
Template policies — use ?principal and ?resource placeholders for dynamic instantiation

6. OctantOS Integration Roadmap

Phase 1: Foundation (Week 1-2)

Define Cedar schema mapping OctantOS domain model (agents, missions, tasks, tools)
Install @cedar-policy/cedar-wasm in packages/api
Create cedar_policies and cedar_policy_sets tables in PostgreSQL
Build Fastify middleware PEP for API authorization
Write initial RBAC policies for 3-tier agent hierarchy

Phase 2: Agent Loop Integration (Week 3-4)

Insert Cedar PEP into agent execution loop (tool invocation interceptor)
Implement structured denial feedback (agent receives reason + hints)
Add ABAC conditions: budget limits, risk levels, autonomy constraints
Enable Record mode — log all decisions without blocking

Phase 3: Advanced Governance (Week 5-6)

Implement Shadow enforcement mode — validate policy accuracy
Add Cedar Analysis CI step — verify safety properties on every PR
Build admin UI for policy management in OctantOS dashboard
Integrate with audit logging system for compliance

Phase 4: Production Enforcement (Week 7-8)

Switch to Enforce mode for Tier 3 agents first
Gradually escalate to Tier 2 and Tier 1
Monitor performance impact (expect < 1ms per evaluation)
Document compliance posture for enterprise customers

7. Risk Assessment

Risk	Likelihood	Impact	Mitigation
Policy misconfiguration blocks legitimate agents	Medium	High	Record -> Shadow -> Enforce progression
WASM performance overhead	Low	Low	Cedar evaluates in sub-ms; negligible
Cedar ecosystem immaturity	Low	Medium	CNCF backing + AWS investment; active development
Schema evolution complexity	Medium	Medium	Schema versioning strategy; backward-compatible changes
Developer learning curve	Low	Low	Cedar syntax is intentionally simple; hours not weeks

8. Conclusion

Cedar is the optimal choice for OctantOS’s agent authorization layer:

Deterministic — provable safety guarantees, not probabilistic LLM guardrails
Performant — 42-60x faster than Rego, sub-millisecond per evaluation
Self-hosted — WASM package, zero external dependencies
Cloud-native — CNCF Sandbox project, Kubernetes integration
AI-agent native — Bedrock AgentCore validates the pattern at AWS scale
Formally verifiable — mathematical proofs of policy properties
TypeScript-friendly — npm packages with full TS support

The architecture pattern is proven: insert a Cedar PDP into the agent execution loop, model agents as principals with budget/risk/autonomy attributes, and use ABAC conditions for governance-first authorization. The progressive Record -> Shadow -> Enforce rollout strategy minimizes risk.

cedar policy engine ai agent authorization