Artificial Intelligence · 20.08.2026, 16:47 UTC
Authoring Dogwood policies from natural language in Amazon Bedrock AgentCore
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | AWS Machine Learning ↗ |
| Veröffentlicht | 20.08.2026 UTC |
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AI agents can automate complex workflows but might take actions that don’t align with your organization’s policies or regulatory constraints if used without proper controls. To address this, we built Policy in Amazon Bedrock AgentCore so teams can implement controls that are applied across agents running in Amazon Bedrock AgentCore. This was recently expanded with new capabilities for enforcing restrictions that constrain agent actions across time, which support policies such as rate limiting, prerequisites and sequential ordering of tool calls, and cumulative effects. These policies are expressed in Dogwood, an open source governance language, and applied to agent actions in real time by the Dogwood monitor built into the AgentCore Gateway, a capability of Amazon Bedrock AgentCore. As part of this new launch, we expand the capabilities of Policy Authoring, an AI-driven tool to convert natural language policy specification documents into syntactically and semantically correct Dogwood formal specifications. With this new feature, you can generate policies that enforce temporal and trajectory constraints, invoke Amazon Bedrock Guardrails services to detect inappropriate content in the semantic meaning of free-form text, as well as policies that place restrictions on the input parameters of tools which were available in the previous version of Policy in AgentCore. Whatever your technical background, you can import policy documents written in natural language directly into the policy in Amazon Bedrock AgentCore to safeguard your deployed agentic systems. In this post, we …