2026 Comparative Analysis: Runtime Policy Enforcement for AI Agent Tool Calls — Applied Technology Index

Executive Summary

Runtime policy enforcement for AI agents is the process of evaluating a proposed tool action at a trusted boundary and allowing, denying, modifying, or escalating it before the action reaches its target. Authentication establishes who is calling. Policy decides whether that principal may invoke this tool, on this resource, with these arguments, in this session, under the current business constraints.

The reviewed systems occupy different layers rather than forming a single winner-takes-all category:

Amazon Bedrock AgentCore Policy is an agent-specific managed enforcement point. It intercepts calls at AgentCore Gateway and combines Cedar authorization, tool-input conditions, policy monitoring, optional content-scoring providers, and Dogwood session-history rules.
Cedar is an embeddable authorization language and engine centered on principal, action, resource, context, schema validation, deny-overrides behavior, and automated analysis.
Open Policy Agent (OPA) is a domain-general policy engine. Rego can evaluate arbitrary structured agent context and return structured decisions through local REST, Go, WebAssembly, or SDK integrations.
OpenFGA is a relationship-based authorization system. It is strongest when the decisive question is whether a user, service, or agent has a modeled relation to a business object, with optional conditions and contextual tuples.
Cerbos is a stateless policy decision point with GitOps-friendly YAML resource and principal policies, roles, derived roles, attributes, conditions, schemas, and batch-oriented resource checks.

The central finding is that agent safety depends more on enforcement placement and decision inputs than on policy-language branding. A perfect rule evaluated only after a payment, email, deletion, deployment, or data export is not a control. The policy enforcement point must sit on every route to the side effect, receive canonical identity and arguments, fail closed when it cannot obtain a valid decision, and emit evidence that joins the decision to the eventual tool result.

Conventional authorization engines can govern agents effectively when the application supplies a normalized action envelope. Agent-specific policy adds value where the system must understand tool schemas, MCP gateway calls, generated policy candidates, model-derived safety signals, or the ordered trajectory of actions in one session. Those additions do not replace ordinary identity, resource authorization, transaction validation, idempotency, or human approval.

A practical production design uses two decisions. First, determine standing authority: may this principal perform this action on this resource class? Second, determine transaction authority: may it perform this exact call now, with these arguments, budget, prior events, risk signals, and approval receipt? Preserve both decisions and their policy versions in the action ledger.

Key Findings

Authentication is input to policy, not policy itself. A valid OAuth token or workload identity does not establish permission for every tool and argument reachable through a gateway.
The model must not be the enforcement point. Prompt instructions and tool descriptions can guide selection, but the model must not be able to bypass, rewrite, or self-approve the rules governing its actions.
Tool-name allowlists are necessary but insufficient. The same transfer, query, messaging, or deployment tool may be safe or unsafe depending on account, tenant, destination, amount, environment, data classification, and approval state.
Cedar and AgentCore emphasize authorization structure. Principal, action, resource, and context provide a clear mapping for tool calls; schemas and automated analysis can detect classes of malformed or unintended policy before enforcement.
OPA offers the broadest decision shape. Rego accepts arbitrary structured JSON and can return more than allow or deny, but the operator must define the agent action schema, integration boundary, and enforcement contract.
OpenFGA is strongest for durable relationships. It answers questions such as whether an agent acting for a user may access a customer, project, document, repository, or account. It is not by itself a complete validator for arbitrary tool payloads or action sequences.
Cerbos provides an application-oriented PDP. Resource policies, derived roles, request attributes, schemas, and CheckResources fit teams that want readable policy files and sidecar or service deployment without building a policy runtime from language primitives.
Point-in-time context is not action history. An expiry condition or a caller-supplied counter is different from an engine that records prior tool events and evaluates sequence, cumulative spend, repeated actions, or output-to-input integrity.
AgentCore’s trajectory-aware profile is distinctive. Its public documentation describes Dogwood temporal rules over session history, including previous-action requirements, cumulative totals, mutual exclusion, and binding a prior output to a later input.
Generated policy is candidate code. Natural-language-to-policy systems can improve authoring, but generated rules still require schema validation, adversarial tests, diff review, staged monitoring, and accountable approval.
Content classification and deterministic authorization are different evidence types. AgentCore can use Guardrails confidence scores in policy, but its documentation explicitly notes that guardrail evaluation is non-deterministic while policy evaluation over supplied inputs is deterministic.
Decision logs can contain sensitive data. Argument-level authorization requires rich inputs; logs therefore need minimization, masking, access controls, retention limits, and stable correlation identifiers.

Methodology

This analysis checked the live Applied Technology Index research index and the local public research collection before topic selection. Existing research covered agent authorization, credential brokering, MCP gateways, human approval, prompt-injection defenses, durable execution, and delegation, but not a focused comparison of runtime policy decision points for individual and sequential tool actions.

Recent public repository activity around agent gateways and policy enforcement, together with newly expanded AgentCore Policy documentation, was used only to select the question. Product and technical claims are grounded in primary materials available on 15 August 2026: Amazon Bedrock AgentCore developer documentation and official samples; Cedar’s official repository; OPA’s official repository and documentation source; OpenFGA’s official repository and documentation source; and Cerbos’s official repository and documentation source.

Each profile was assessed on ten criteria:

Decision model: boolean authorization, structured decision, relationship check, or action-specific effect.
Enforcement placement: embedded library, sidecar, policy service, application middleware, or managed agent gateway.
Identity model: principal identifiers, token claims, groups, roles, workload identities, and delegated-user context.
Resource model: typed entities, relationship tuples, application resource kinds, or arbitrary structured input.
Argument awareness: whether policy can evaluate canonical tool input rather than only tool names.
State awareness: point-in-time context, persisted relationships, external facts, or ordered session history.
Analysis and testing: schema validation, automated reasoning, unit tests, model tests, and staged enforcement.
Decision evidence: policy revision, rule identity, inputs, outputs, reason metadata, trace correlation, and masking.
Portability: coupling to an agent gateway, cloud, language, runtime, application model, or policy API.
Failure semantics: default deny, deny overrides, undefined decisions, unavailable PDP behavior, and enforcement bypass risk.

This is a documented-capability and architecture comparison. No equivalent agent application, policy corpus, or latency benchmark was run across all systems. The analysis does not rank policy expressiveness formally, prove implementation correctness, or compare commercial support, hosted-service pricing, regional availability, throughput, tail latency, consistency, or vulnerability rates.

Comparative Analysis Table

System	Primary decision model	Agent-relevant strength	State and context model	Deployment and enforcement profile	Main limitation
Amazon Bedrock AgentCore Policy	Permit, forbid, and documented output-suppression effects for gateway actions	Native interception of agent-to-tool traffic; tool-schema-aware Cedar authoring; argument conditions; monitoring; optional Guardrails signals; Dogwood temporal rules	Current principal/action/resource/input, provider signals, and session-scoped recorded action history	Managed policy engine associated with AgentCore Gateway; monitor or enforce at the gateway boundary	AWS and AgentCore Gateway coupling; evolving features and language surface; managed boundary does not cover side channels that bypass the gateway
Cedar	Fine-grained authorization over principal, action, resource, entities, and context	Clear mapping from agent identity and tool operation to typed authorization; schema validation and automated-analysis orientation	Point-in-time entity data and request context supplied to the engine	Embedded Rust crate, CLI, or WebAssembly integration inside an application or policy service	Base Cedar does not record agent trajectories, operate a gateway, or define an agent action ledger by itself
Open Policy Agent	Arbitrary structured decision from Rego over structured input and data	Can express tool, argument, budget, environment, provenance, and risk rules in one normalized decision document	Point-in-time input plus loaded data; history can be supplied by the application or another data source	REST daemon or sidecar, Go APIs/SDK, WebAssembly, or custom evaluator; application enforces the returned decision	Domain-general rather than agent-native; operator must design schemas, data freshness, fail-closed integration, and safe structured outputs
OpenFGA	Relationship check: user has relation to object, derived through an authorization model and tuples	Strong tenant, ownership, group, delegation, hierarchy, and object-access layer for agents acting for users or services	Persisted relationship tuples plus optional condition context and contextual tuples	Networked HTTP/gRPC authorization service or embedded Go library; application calls Check or BatchCheck	Relationship authorization is not a complete policy for arbitrary payload values, cumulative budgets, content signals, or ordered tool trajectories
Cerbos PDP	Per-action allow or deny for application resources, with optional outputs and metadata	Readable resource policies for principals that may be users, apps, services, or bots; roles, derived roles, attributes, schemas, and batch checks	Stateless request evaluation over principal, resource, action, and supplied attributes	Self-hosted sidecar/service, system service, serverless function, or embedded distribution profile; application enforces decisions	Application must map every tool call into resources/actions, supply trustworthy attributes, and persist any trajectory state outside the PDP

Observed Profiles

Amazon Bedrock AgentCore Policy: gateway-native action and trajectory enforcement

AgentCore Policy attaches a policy engine to an AgentCore Gateway and evaluates requests before allowing access to gateway tools. The official documentation describes this as enforcement outside the agent’s code. That placement matters: a manipulated model can propose a forbidden call, but it should not be able to change the gateway’s decision.

The base authorization model uses Cedar. Policies can restrict a principal to a particular gateway action and inspect tool input through context fields. Official samples demonstrate identity claims surfaced as principal tags and argument-level conditions such as region, amount, risk, or governance approval. The sample also documents ENFORCE as default deny and MONITOR as a mode that permits traffic while logging decisions, making monitor-to-enforce migration an explicit operating pattern.

Natural-language authoring generates candidate Cedar against gateway tool schemas and runs validation and automated analysis. This can lower authoring friction and reduce incorrect action or resource names. It does not turn English into an approval authority. Generated policy should enter the same reviewed, versioned, tested deployment path as hand-written code, and findings should not be ignored as routine scaffolding.

AgentCore can also consult Bedrock Guardrails during policy evaluation. The documented categories include content filters, prompt-attack detection, and sensitive-information detection. These providers return confidence scores that policies compare with thresholds. The developer guide distinguishes the layers: provider scoring is non-deterministic, while the policy evaluator deterministically applies the configured rule to the values it receives. Thresholds therefore require a representative test set, precision-recall analysis, monitoring, and a fallback policy for provider failure.

The most agent-specific capability is temporal policy. Official samples describe Dogwood as extending Cedar with history-aware conditions over events recorded in the same session. Examples require an earlier balance lookup before transfer, bind a prior output account to a later transfer input, sum trade amounts over a time window, cap repeated actions, and prevent contradictory decisions on the same claim.

Trajectory awareness addresses a real gap in point-in-time authorization. Ten individually permitted transactions can violate a cumulative budget. A transfer can be valid in isolation but invalid because its destination differs from the account returned by a preceding verified lookup. A later action can contradict an earlier committed decision. The tradeoff is state complexity: session identity, event recording, retries, concurrency, partial failures, retention, and cross-session business state all become part of authorization correctness.

Cedar: analyzable authorization for a normalized action envelope

Cedar is purpose-built for authorization. Its standard request shape—principal, action, resource, and context—maps cleanly to an agent action envelope. The principal may be a user, workload, agent instance, or delegated pair; the action is a canonical tool operation; the resource is the target business object or gateway; and context carries bounded transaction facts.

The official repository emphasizes separation from application code, schema validation, fast real-time evaluation, and automated analysis. Those properties are valuable for agent systems because tool catalogs and argument schemas change. A schema can reject policies that refer to invalid entity types or actions before runtime, while symbolic analysis can support deeper questions about whether a policy is overly broad or inconsistent.

Base Cedar remains an authorization engine, not an agent platform. The application must authenticate the principal, build trustworthy entities and context, place evaluation before every side effect, enforce the decision, record the outcome, and prevent alternate routes. History-dependent rules require an external state model or a trajectory-aware extension such as the Dogwood profile documented by AgentCore.

A sound mapping avoids using a single generic invoke action for every tool. Model stable operations such as invoice.read, payment.create, repository.merge, or environment.deploy; bind them to resource identifiers and canonical typed inputs; and preserve the evaluated policy version with the tool receipt.

Open Policy Agent: domain-general structured policy decisions

OPA decouples policy decision-making from enforcement and accepts arbitrary structured data. Rego can therefore evaluate a rich agent envelope containing actor, delegated user, tenant, tool, arguments, resource, environment, data classification, cost estimate, approval receipt, provenance, and risk signals. A decision can be boolean or a structured object containing obligations, reasons, required approval, filtered capabilities, or limits.

The official integration documentation supports several placements: a local REST daemon or sidecar, Go SDK or lower-level API, and WebAssembly. A local PDP can reduce network dependence and latency, while centrally distributed bundles provide a management plane. This flexibility is useful for heterogeneous agent runtimes and gateways.

Flexibility also transfers design responsibility to the operator. OPA does not supply a universal agent-tool schema. Teams must standardize input names and types, define how undefined or malformed decisions fail, decide whether obligations are enforceable or merely advisory, and make all tool routes consult the same policy. External data must have explicit freshness and failure semantics.

OPA’s decision logs can include the queried policy path, input, result, bundle revision, decision ID, trace and span IDs, timestamp, metrics, and rule labels. The documentation also provides masking and erasure controls. For agent systems, these controls are essential because the same tool arguments needed for fine-grained authorization may contain personal data, secrets, prompts, or financial details.

Rego unit tests and data-driven test cases support policy-as-code release gates. A production agent corpus should test not only positive and negative access cases but malformed identities, cross-tenant resources, boundary amounts, stale approvals, duplicated requests, missing attributes, untrusted model-supplied fields, and PDP unavailability.

OpenFGA: relationship authorization for agents acting on business objects

OpenFGA is designed to answer whether a user has a modeled relation to an object. Its ReBAC model is particularly useful when agent authority derives from ownership, organization membership, project roles, repository teams, customer assignments, folder hierarchy, or explicit delegation.

An agent acting for a user can ask whether that user is an editor of document:Z, an administrator of organization:acme, or an operator of environment:production. A service agent can also be represented as a subject where the business model permits it. Check and BatchCheck APIs make these decisions consumable by a gateway or application policy layer.

OpenFGA conditions add request and persisted context to relationships. Official documentation demonstrates expiring grants and notes that conditions can support temporal access windows, IP or geographic constraints, entitlements, and resource attributes. This use of “temporal” means evaluating time context associated with a relationship; it is not the same as recording an ordered sequence of agent tool events and aggregating their effects.

OpenFGA should therefore be treated as a strong relationship fact source, not the entire transaction policy. A gateway may first ask OpenFGA whether the delegated user can operate on an account, then use Cedar, OPA, Cerbos, application validation, or a managed policy layer to decide whether this exact transfer amount and destination are permitted now.

Official model-testing documentation supports checked expectations for relationships, conditions, object listing, and user listing. Those tests should be versioned alongside the application mapping from tool resources to OpenFGA objects; a correct model queried with the wrong tenant or object ID still produces an unsafe system.

Cerbos: application-oriented resource policy decision points

Cerbos models principals, resources, actions, roles, derived roles, attributes, and conditions in YAML policy files. The principal may represent a user, application, service, or bot, which allows an agent host or workload to participate directly in the authorization model.

Resource policies group rules by application resource kind. A call to CheckResources asks which actions a principal may perform on one or more resource instances. Conditions can inspect principal and resource attributes, schemas can validate supplied attributes, and derived roles can express relationships such as owner or moderator without hard-coding them in application branches.

This is a practical fit for teams that want a self-hosted stateless PDP and GitOps-style authoring. Cerbos documents deployment as a service, sidecar, system service, or serverless function. Its metadata can identify matched policy and effective roles, supporting an auditable explanation path.

For agent use, the application still needs an adapter that converts every proposed tool call into canonical Cerbos resources and actions. Tool descriptions are not enough. The adapter must resolve the actual tenant and resource from trusted application state, not accept model-provided ownership claims. Cumulative spend, prior approvals, and action sequences require an external ledger whose derived facts are supplied to policy or enforced by a separate trajectory-aware layer.

Production Enforcement Blueprint

Normalize every proposed action

Before policy evaluation, convert framework-specific tool calls into a stable envelope containing:

authenticated user, workload, agent, tenant, and delegated-actor identities;
canonical operation and tool version;
target resource type, identifier, owner, tenant, and classification;
typed and normalized arguments, with defaults resolved;
session, run, task, tool-call, approval, and idempotency identifiers;
estimated cost, cumulative budget, rate, destination, environment, and reversibility;
provenance and integrity references for inputs that influenced the action;
policy-set version and required decision deadline.

Do not let the model declare that a resource is owned, an approval exists, or a budget remains. Resolve those facts from trusted systems.

Separate standing authority from transaction authority

Use relationship or identity policy to determine whether the actor can operate on the resource class. Then evaluate the exact transaction, including arguments and current state. A repository maintainer may have standing authority to merge, yet a production branch merge may still require passing checks, a human approval, a signed artifact, and a bounded change set.

Place enforcement on the unavoidable path

The gateway, API, database procedure, cloud control plane, or transaction service must enforce the result before commitment. If an agent can call the target through another SDK, shell, browser session, cached credential, or direct network route, the policy gateway is advisory rather than authoritative.

Fail closed and preserve idempotency

Define behavior for PDP timeout, unavailable relationship data, malformed context, unknown tool versions, missing resource identifiers, and stale policy bundles. Consequential writes should deny or pause rather than silently bypass. Retry by stable operation ID and query transaction status before repeating a mutation.

Bind approvals and prior events cryptographically or by immutable reference

An approval should name the exact action, canonical arguments or digest, resource, actor, amount or limit, expiry, policy version, and approver. A temporal rule should reference committed event IDs and tool receipts, not only conversational claims that something happened.

Log the complete decision-to-effect chain

Retain the proposed envelope, decision ID, matched rule or reason, policy revision, decision time, enforcement point, approval receipt, downstream request ID, result, and final business transaction ID. Mask or omit sensitive values while preserving hashes or typed summaries needed for audit and replay.

Selection Framework

Choose AgentCore Policy when AgentCore Gateway is already the mandatory tool boundary and native tool-schema mapping, managed monitoring, Guardrails-derived signals, natural-language-assisted authoring, or session-history enforcement materially reduce implementation work.

Choose Cedar directly when the application needs a focused, analyzable authorization language embedded close to the action path and the team is prepared to operate identity, context, policy distribution, logs, and any history state separately.

Choose OPA when policies span heterogeneous runtimes and domains, the decision needs arbitrary structured inputs or outputs, and the platform team can define and govern a shared agent action schema and policy-management plane.

Choose OpenFGA when object relationships, hierarchy, teams, ownership, tenancy, and delegation are the hard part. Pair it with transaction policy for argument limits, content risk, budgets, approvals, and trajectories.

Choose Cerbos when teams want application-resource authorization expressed in accessible YAML, deployed as a stateless PDP, with roles, derived roles, ABAC conditions, schemas, and GitOps-friendly policy management.

Most mature systems will combine layers. OpenFGA may answer whether the user has access to the account; OPA, Cedar, or Cerbos may evaluate the proposed transaction; a gateway may enforce the decision; a human-approval service may authorize exceptional risk; and a durable ledger may track cumulative actions and results. Composition is safe only when identifiers, policy versions, and failure semantics remain consistent across layers.

Limitations

This analysis relies on public documentation and open repositories available on 15 August 2026. AgentCore Policy, Dogwood temporal policy, and Guardrails-in-policy features are evolving, and public samples may change independently of deployed service behavior. Cloud features can differ by region, account, release stage, and service quotas.

The compared systems are not direct substitutes. AgentCore Policy is a managed agent-gateway feature; Cedar is a language and engine; OPA is a general policy engine and management runtime; OpenFGA is a relationship-authorization system; and Cerbos is an application-oriented policy decision point. The table compares operating roles for agent tool governance, not product completeness or market maturity.

No common policy suite or live tool workload was executed. The analysis does not measure decision latency, throughput, availability, consistency, cache behavior, policy-distribution lag, authoring error rate, natural-language generation accuracy, guardrail classification accuracy, temporal-event correctness, operating cost, or ease of incident response.

Documentation establishes intended capabilities, not secure deployment. An external PDP can still be bypassed by alternate credentials or routes. A schema can validate types while business facts are stale. A correct relationship model can be queried with the wrong object. A temporal history can be incomplete under retries or concurrency. A logged decision does not prove that the downstream action matched the evaluated request.

The analysis does not constitute a formal security assessment. High-consequence deployments require threat modeling, identity and delegation design, policy review, negative testing, fault injection, privacy controls, penetration testing, transaction-level reconciliation, and domain-specific legal or compliance review.