An open-source runtime verification framework monitors AI agents during execution, checking whether their actions comply with predefined rules to improve reliability, safety, and operational correctness.
AWS has introduced Dogwood, an open-source runtime verification framework designed to monitor the behaviour of AI agents while they are executing tasks. Rather than evaluating outputs after execution, the framework continuously verifies whether an agent’s actions conform to predefined specifications, allowing incorrect or unsafe behaviour to be detected as it occurs.
Dogwood implements the runtime verification paradigm by comparing runtime events with formalised behavioural policies. In this way, developers are provided with the ability to check for any violation of policies, misuse of tools, or any deviation from the workflow without changes to the AI model itself.
The proposed framework is meant to be used alongside existing implementations of AI agents, making it possible for verification to take place independently of the reasoning process of the agent itself. Developers may specify rules describing the desired behavior, allowing interactions with third-party systems to be monitored.
The framework is released as an open-source project, allowing developers to inspect, modify, and extend the framework for their own applications. Dogwood is developed for use by the community in order to facilitate the development of dependable agent AI systems in different environments.
Dogwood applies runtime verification to AI agents by monitoring their behavior against predefined specifications during execution. The framework is designed to operate without modifying the underlying AI models or application logic.
