intelligence-module-patterns

You can architect intelligence modules by coordinating synthesis and query components through a centralized ModuleRegistry, applying BaseModule inheritance and lifecycle checks to orchestrate data deterministically across sources.

Standard architectural patterns include defining Analyst, Scientist, Guide, and Oracle roles to handle observation, hypothesis, synthesis, and querying interactions with calculation modules via a ModuleRegistry.

Auto-registration is achieved by initializing modules through BaseModule.__init__, which enables cohesive ecosystem growth and automatic registration into the ModuleRegistry without manual entry.

Yes, the pattern provides deterministic workflows for synthesis and querying that include specific lifecycle management and safety considerations to ensure secure data orchestration across modules.

Yes, the intelligence module patterns support deferred imports alongside API patterns for synthesis and querying, ensuring scalable component integration within the ModuleRegistry architecture.

The best way is using a centralized ModuleRegistry where calculation modules produce data and intelligence layer components like Query and Synthesis consume it, maintaining a clear separation of concerns.