[Problem]
In dynamic AI environments, module logic often needs to be updated without restarting the host process (hot-reloading). However, simply replacing the module instance causes any transient "in-memory" state (like a session buffer or a cache) to be lost.
[Why]
Zero-downtime updates are critical for production-grade AI agents. If an agent is in the middle of a complex multi-step task, restarting the service would break the chain of thought and lose context.
[How]
- Lifecycle Hooks: Added
on_suspend() and on_resume() to the Module interface.
- Migration Algorithm: Defined a formal state-transfer protocol where the old instance serializes its state into a
StateContainer which the new instance consumes upon loading.
- Renumbering: Updated the Protocol Specification to accommodate the new hot-reload phase in the module lifecycle.
[Problem]
In dynamic AI environments, module logic often needs to be updated without restarting the host process (hot-reloading). However, simply replacing the module instance causes any transient "in-memory" state (like a session buffer or a cache) to be lost.
[Why]
Zero-downtime updates are critical for production-grade AI agents. If an agent is in the middle of a complex multi-step task, restarting the service would break the chain of thought and lose context.
[How]
on_suspend()andon_resume()to theModuleinterface.StateContainerwhich the new instance consumes upon loading.