Part of what makes AI agents so useful is their ability to interact with the external world by running tools. But these tool calls are also the source of the biggest risks when it comes to making agents safe to use. 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. By enforcing rules about how agents may use tools, we get rigorous guarantees about the ways agents can affect the external world. To do this effectively, we need a way to precisely specify and enforce rules about agent behavior. Today, we’re releasing Dogwood, an open source governance language designed for agents and their tools.
We previously made the case for regulating agent tool use with AgentCore Policy, the layer in Amazon Bedrock AgentCore that decides, on every tool call, whether an agent’s action is allowed. AgentCore Policy launched using Cedar as the language those…
https://aws.amazon.com/blogs/opensource/introducing-dogwood-runtime-verification-for-ai-agents/
