Amazon Bedrock AgentCore has introduced a new capability for authoring policies. This feature allows users to convert natural language policy documents into Dogwood policies, which can then enforce controls across AI agents. The AWS Machine Learning Blog details how this process works, providing examples and best practices.
Key Points
- Amazon Bedrock AgentCore now supports authoring Dogwood policies from natural language input.
- This policy authoring capability allows for the enforcement of controls across AI agents.
- The new policies include the ability to implement time-based constraints.
- The AWS Machine Learning Blog provides worked examples and best practices for this feature.
- AI agents can take actions that may not align with an organization's policies.
Context
According to the AWS Machine Learning Blog, AI agents can sometimes perform actions that do not match an organization's established policies. The new policy authoring feature in Amazon Bedrock AgentCore addresses this by enabling teams to define and enforce controls. This includes new time-based constraints, as highlighted in the blog post.
Another post from the AWS Machine Learning Blog describes how AWS Professional Services utilizes a multi-agent framework built on Amazon Bedrock AgentCore to automate enterprise cloud migrations. These agents manage tasks such as discovery, infrastructure as code generation, portfolio governance, and post-migration operations, significantly reducing infrastructure as code development time.
Why It Matters
This development provides builders with a method to integrate specific organizational policies, including temporal restrictions, directly into the operational framework of AI agents. It offers a structured way to manage agent behavior and ensure alignment with governance requirements, which is particularly relevant for complex, automated processes like cloud migrations.
What To Do
- Review the AWS Machine Learning Blog post on "Authoring Dogwood policies from natural language in Amazon Bedrock AgentCore" for detailed examples.
- Note the best practices outlined for converting natural language into Dogwood policies.
- Consider how time-based constraints could be applied to existing or planned agent workflows.
- Watch for further guidance on integrating these policies with agentic AI frameworks.