Abstract

This disclosure presents a dual-AI orchestration system that solves the persistent context loss problem in AI-assisted software development. Current AI assistants maintain conversation memory only within individual sessions, requiring developers to repeatedly re-explain project context when resuming work after hours, days, or weeks. This creates significant productivity loss and escalating API costs as complete project context must be reloaded with each session.

The disclosed system coordinates a local AI instance with cloud-based AI services through hierarchical knowledge base integration. The local AI manages a structured vault containing project documentation, code history, and conversational logs organized in hierarchical layers from high-level summaries to detailed technical specifications. When queries arrive, the system intelligently routes them: simple retrieval and routine tasks execute locally at zero cost, while complex reasoning tasks leverage cloud AI with precisely- targeted context injection.

Context degradation detection monitors conversation quality through semantic similarity analysis, circular pattern detection, and token threshold tracking. Upon detection, automatic checkpoint creation preserves complete project state, enabling seamless restoration in fresh sessions. Field testing demonstrates 70-90% reduction in cloud API costs while maintaining full project context across gaps of days to months between development sessions.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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