AI Agent · Rust · Amazon · The Register
AWS offers local, open source leash for agent harnesses
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Dogwood Local Engine checks AI tool calls against user-defined temporal rules before they run.
Key facts
- In tests simulating sessions from five minutes to 12 hours, DLE evaluation time was around 20 microseconds with a 15-minute window at the 12-hour mark, rising to about six milliseconds with a 24-hour
- AWS gives the example of controlling coding agent Git pushes using DLE, defining a policy that only allows a push when the most recent test run has passed, and requiring that pass to have occurred
- Dogwood Local Engine checks AI tool calls against user-defined temporal rules before they run
- When AWS introduced Dogwood, it open sourced the language and reference tooling and added support for it to Amazon Bedrock AgentCore; DLE’s release makes the policy engine directly embeddable
Summary
If you don't want your AI agents running out of control and changing files or transferring data outside your rules, AWS has published an open source software library that it claims can add a new layer of policy control. As a dog’s harness is useless without a leash to keep it under control, the Dogwood Local Engine promises to put AI agent tool calls on a leash, blocking actions that don’t meet user-defined temporal conditions. The DLE comes in the form of a Rust library that can be embedded into an agent’s harness or gateway and, to boil a long explanation down to a single sentence, simply issues allow/deny verdicts each time an agent tries to make a tool call.