The current design is intentionally lean, but there are clear avenues for growth. A curated, versioned catalog of ontology and axiology axes, delivered as JSON with editing tools, would make extensions safer and more portable. LSH parameters could be benchmarked per domain, perhaps with an adaptive band/width tuner that remains deterministic once chosen. TranslatorBridge packs for multiple languages, with pinned prompts and cached models, would open new domains without sacrificing reproducibility. A small GUI to explore theory stacks, conflicts, and provenance traces would help non-developers audit and debug the system.

Clustering could benefit from entropy-based heuristics and drift detection to avoid over-splitting or over-merging as data grows. If constraints ever allow, native or WASM kernels could accelerate hot loops for very large deployments while keeping logic identical. Domain starter packs—health, finance, safety, narrative consistency—would speed onboarding. Finally, integration recipes for RAG with popular LLM frameworks would show how to pair AGISystem2’s determinism with generative fluency in production settings.

All of these ideas preserve the core principles: determinism, explicit geometry, and auditability. They simply make the system easier to extend, faster to run, and friendlier to adopt.