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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteHow do you authorize an AI agent beyond authentication? Verifying who or what is making a request is only the first step. The harder question is whether that principal may take a specific action on a specific resource right now, given what it has already done. AWS Dogwood, announced on 6 August 2026 as an open-source governance language for agents and their tools, targets that second question by letting a policy decision depend on recent agent events and their outcomes rather than on the current request alone.
Why authentication does not settle the question
Authentication establishes which principal is acting. Authorization decides whether that principal may perform a particular operation on a resource under the applicable policy. The two controls answer different questions, and an agent system needs both. A tool-using agent may act on its own behalf or explicitly for a user, so a verified identity still leaves open whether a proposed action, such as sending a payment, deleting a record, or pushing code, is allowed in the current state of the workflow.
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AWS’s guidance on agent identity makes the same separation. Its Agentic AI Lens calls for verifiable agent identities, separate agent and human identities, propagation of signed user context when an agent acts on behalf of a user, least privilege, and continuing permission reviews. (AWS, Agent identity and permission management, Agentic AI Lens) None of those controls, on its own, decides whether a particular tool call should happen at this point in a session.
The point-in-time gap Dogwood targets
Most policy engines evaluate a request in isolation. They see the principal, the action, the resource, and the context attached to that request, and they return a decision. AWS describes Cedar as evaluating point-in-time requests independently in this way. (AWS Open Source Blog, 6 August 2026)
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That model is awkward for agents. A sale of shares may be safe only if an approval for the same stock and quantity was granted and that approval reached an approved outcome. A code push may be safe only if tests passed recently and nothing has failed since. A request-only check cannot see those prerequisites unless the surrounding application reimplements them outside the policy layer. Dogwood’s contribution is to express those conditions as policy.
What Dogwood adds: temporal conditions over agent history
In AWS’s account, Dogwood keeps the existing Cedar model for point-in-time decisions, can evaluate existing Cedar policies, and adds temporal conditions that refer to an agent’s recent request and response events. AWS says the language was released under Apache 2.0. Not every Cedar installation automatically gains Dogwood behavior; the temporal conditions are the part that depends on the event history described below.
AWS identifies three kinds of rule that temporal conditions can express.
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Binding approval to a later action
An approval event can be required before a later action is allowed, and the policy can check that the approval refers to the same parameters. AWS’s example requires an approval for the same stock and share quantity, with an approved outcome, before a sale is permitted. The sale request is evaluated against the recorded approval, not only against the sale request’s own fields.
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Enforcing ordering and timing
A rule can require one event to precede another and can bound the time between them. AWS’s example allows a code push only after a successful test run within the preceding 15 minutes, and only if no failure has occurred since that successful run. The push is therefore blocked when the test result is stale or when a later failure invalidates it.
Limiting frequency of use
Temporal conditions can also restrict how often a tool is used within a window. This turns a per-request check into a constraint over the agent’s recent activity, which is useful for actions where a single call is acceptable but a burst of calls is not.
These examples show the core idea: the permission decision can depend on action history and on outcomes, not only on the attributes of the pending call.
Where the Dogwood Local Engine fits
AWS announced the Dogwood Local Engine on 30 September 2026, also under Apache 2.0. (AWS Open Source Blog, 30 September 2026) It is a library that evaluates a stream of events and returns an allow or deny verdict. It is a component you embed, not a service that controls the agent by itself.
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What the engine does
- Keeps an ordered, durable event record.
- Persists each event before evaluating it.
- After a restart, rebuilds state from snapshots and later log entries.
- Serializes concurrent submissions.
What the engine does not do
AWS says the engine does not execute or block the tool call itself. It also says the library does not provide operating-system isolation. Those responsibilities belong to the surrounding system, described in the next section.
How policy updates interact with history
Updates and events are written to the same ordered log, so requests made after a policy update are evaluated against the complete new policy set. A newly added temporal clause considers events that arrive after the update; AWS documents that it is not applied retroactively to earlier events. Do not assume that a new rule inspects the entire earlier session. If your workflow needs a rule to account for prior activity, the events that rule depends on must be recorded in the log, and the policy designer should confirm that the clause’s history window starts where intended.
What the surrounding system must still do
The engine produces verdicts. Stopping an action and keeping the record trustworthy are the harness’s job. AWS states that the harness or equivalent enforcement layer must:
- Intercept every relevant tool call before it executes.
- Submit the request event and, after execution, the response event to the engine.
- Prevent execution when the engine returns a deny verdict.
- Ensure event integrity, so that recorded history reflects what actually happened.
- Protect the engine’s state from manipulation.
A harness that skips a tool path, drops a response event, or lets an agent write to the event log has weakened every temporal rule that depends on that history. Coverage of all relevant tools is therefore a precondition for the policy to mean anything.
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AWS’s guidance on securing agent tool use lists further controls that remain necessary alongside any temporal policy: 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, Secure agent tool usage, Agentic AI Lens) History-aware policy narrows what an agent may do at the tool-call boundary. It does not replace those controls, and it does not by itself prevent prompt injection or privilege escalation.
Reading the performance figure correctly
AWS’s Local Engine post reports one performance result. In a simulation, it compared 100 policies across five Git actions, using a coarse-grained action schema and a fine-grained one. AWS reports identical verdicts in the simulated sessions and roughly fivefold faster evaluation for the fine-grained schema when evaluating push requests. The result is specific to that simulated setup: the schema granularity and the policy set both shape it. It is not an independent benchmark, a measure of production latency, or a guarantee for other workloads.
Comparing point-in-time and history-aware authorization
The table below organizes the differences AWS describes. Where AWS does not state a value for a dimension, the cell says so.
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|---|---|---|
| Decision context | Current request only | Current request plus recent request and response events |
| Rule shape | Single-request constraints | Prerequisites, sequences, outcomes, and time windows, plus frequency limits |
| Integration boundary | Policy decision | Policy decision plus harness interception, enforcement, and trustworthy event submission |
| State handling | Not stated in the AWS Dogwood post for stateless checks | Durable ordered event log, persistence before evaluation, snapshot and log replay after restart, serialized concurrent submissions (AWS Local Engine post) |
| Policy updates | Not stated in the sources for point-in-time evaluation | Updates ordered in the same log; new temporal clauses consider events after the update |
| Identity and scope | Complements agent identity, delegated user context, and least privilege | Also complements those controls; does not replace them |
| Reported performance | Not stated in the sources | Roughly fivefold faster for fine-grained schema on push requests in AWS’s simulation |
Deciding whether you need history-aware authorization
Use this check before adopting temporal policy for a tool:
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- Does a permitted action depend on an earlier approval, test, or check that must still be valid?
- Does the safe outcome of a call depend on the response of a previous call, not only on its arguments?
- Would a burst of individually allowed calls create a risk you need to bound over a time window?
- Can you intercept every path by which the agent reaches the tool, and record both request and response events?
- Do you have identity, least-privilege credentials, input and output validation, human approval for high-risk mutations, and monitoring in place?
If the answer to the first three questions is no, a request-level policy may be sufficient for that tool. If the fourth or fifth is no, temporal rules will not be reliable yet, whatever policy language you use.
Dogwood’s announcement quotes its authors on the design principle behind it: “The best way to address these risks in a dependable and reliable manner is to put a layer of control at the tool-call boundary that regulates what an agent is allowed to do.” (AWS Open Source Blog, 6 August 2026, by Marc Brooker, Joseph Tassarotti, and Jean-Baptiste Tristan.) AWS identifies Brooker as a VP and Distinguished Engineer at AWS, Tassarotti as an Amazon Scholar in AWS’s Automated Reasoning Group and an assistant professor at New York University, and Tristan as a Senior Principal Applied Scientist at AWS Agentic AI.
Dogwood is a useful addition to the authorization stack because it lets the policy layer see what an agent has already done. It is not an identity system, a sandbox, or a safety guarantee. Its value depends on the harness that routes every relevant call through it and on the controls that surround it.
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