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How do you authorize an AI agent beyond authentication? Verify the agent’s identity, then evaluate each proposed tool action against policy and the agent’s relevant recent actions. AWS Dogwood addresses that second, history-aware step: it adds temporal conditions to authorization decisions, so a tool call can depend on what happened earlier, when it happened, and whether it succeeded. Dogwood does not identify agents or enforce denials by itself; those remain responsibilities of the surrounding system.
Authentication identifies the actor; authorization decides what it may do
Authentication establishes which principal is making a request. Authorization determines whether that principal may perform a particular operation on a resource under the applicable policy. An authenticated agent may still be unauthorized to sell shares, push code, or invoke another tool in a particular situation.
For an agent, the decision may need context beyond the current request: for example, whether an approval was recorded for the same transaction, or whether tests passed recently. AWS describes Cedar as evaluating point-in-time requests independently. Dogwood adds temporal conditions that can consider recent request and response events alongside Cedar policies. AWS announced Dogwood on 6 August 2026 as an open-source governance language for agents and their tools, released under Apache 2.0. AWS’s Dogwood announcement
What Dogwood changes in a tool-call decision
A point-in-time policy asks whether the current request meets the relevant conditions. A temporal policy can also ask whether the agent’s recent history satisfies a prerequisite. That makes it possible to express workflow rules in authorization rather than treating every call as an isolated event.
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| Aspect | Point-in-time evaluation | Dogwood temporal conditions |
|---|---|---|
| Decision context | The current request | The current request plus relevant prior events and outcomes |
| Rule shape | Conditions on a single action | Prerequisites, ordering, outcomes, and time windows |
| Example | Whether a principal may invoke a tool for a resource | Whether a sale follows an approved matching request, or a code push follows a recent successful test |
The distinction is not that Dogwood replaces Cedar. AWS says Dogwood supports evaluating existing Cedar policies and adds temporal conditions. That does not mean every Cedar installation automatically supports Dogwood; the policy capability depends on the specific integration.
Approval tied to a later transaction
AWS’s example requires approval for the same stock and share quantity, with an approved outcome, before a sale is allowed. A generic approval event is not enough: the policy can connect the earlier approval to the later action’s details and outcome.
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Recent successful tests before a code push
Another AWS example permits a code push only after a successful test run within the preceding 15 minutes, provided there has been no failure since that successful run. The timing window and event outcome matter, not just the fact that a test occurred at some point.
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AWS announced the Dogwood Local Engine on 30 September 2026, also under Apache 2.0. It is a library that evaluates an event stream and returns an allow or deny verdict. Its role is to maintain and evaluate temporal policy state; it is not the component that executes or blocks the tool call. AWS’s Local Engine announcement
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The engine’s operational model matters because history-based decisions rely on an ordered record of events. AWS documents a durable event record, persistence before evaluation, serialized concurrent submissions, and recovery by rebuilding state from snapshots and later log entries after a restart.
The harness must enforce the verdict
An agent harness or equivalent enforcement layer must intercept every relevant tool call, submit request and response events, and prevent execution when the engine returns deny. If the harness fails to intercept a call, the temporal policy cannot govern that call. The harness also has to protect event integrity and the engine’s state from manipulation. AWS explicitly notes that the Local Engine library does not provide operating-system isolation.
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Policy updates and event history
A new temporal clause applies to events arriving after the policy update; AWS does not describe it as retroactively applying to earlier events. Policy updates and events are ordered in the same log, so requests after an update see the complete updated policy set. Systems that depend on a particular history window should account for this update boundary when designing workflows and testing policy changes.
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What Dogwood does not replace
Dogwood belongs at the action-policy layer. It does not establish the agent’s identity, carry a user’s authority into an agent request, constrain credentials, validate tool inputs or outputs, or monitor activity. AWS’s Agentic AI Lens recommends verifiable agent identities, distinct agent and human identities, signed user context when acting on a user’s behalf, least privilege, and continuing permission reviews. AWS guidance on agent identity and permission management
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AWS separately recommends declarative authorization before every tool invocation, policy and schema checks, human checkpoints for high-risk mutations, rate limits, reviewed and registered tools, and end-to-end observability. AWS guidance on secure agent tool usage
History-aware authorization can express restrictions based on earlier actions at the tool-call boundary. It is not, by itself, a guarantee that an agent is safe, nor does the evidence establish that it prevents prompt injection or privilege escalation. Those risks still call for identity, permission, validation, enforcement, and monitoring controls around the policy engine.
Performance claims need their test context
In its Local Engine post, AWS reports roughly fivefold faster evaluation for push requests with a fine-grained action schema than with a coarse-grained schema. The comparison used 100 policies across five Git actions; AWS says verdicts were identical in the simulated sessions. This is an AWS-reported simulation result for that setup, not a universal benchmark or service guarantee. It illustrates that schema granularity can affect evaluation time; it does not establish how another workload will perform.
When Dogwood is relevant to an agent design
Dogwood is most relevant when permission to take an action depends on a prior event, its result, its relationship to the proposed action, or how recently it occurred. Before adopting temporal rules, decide which events are authoritative, which calls must be intercepted, how denials are enforced, and how policy changes affect in-flight workflows. Evaluate those responsibilities alongside identity, delegated user context, least privilege, input and output validation, high-risk human checkpoints, rate limits, and observability.
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