context-engineering-basics

Context engineering optimizes token usage by dynamically discovering, compressing, and caching relevant information for AI agents. This structured approach enables systems to maintain high-quality, context-aware responses across long conversations while operating within strict token limits.

Implement just-in-time (JIT) context loading by initially loading only essential context for a multi-CLI dialogue, then progressively expanding with relevant references as the conversation evolves. This on-demand incremental loading balances accuracy and token efficiency.

Memory consolidation manages long-term context by organizing data into working, episodic, and semantic memory structures. This categorization ensures continuity across multiple sessions and allows agents to adapt to new tasks without exceeding token limits.

Yes, dynamic context discovery identifies and loads only the most relevant knowledge segments at runtime. Combined with context compression to prune historical content, it allows AI agents to handle long conversations effectively despite limited token availability.

The best way to structure memory for multi-CLI agents is using a tiered system of working, episodic, and semantic memory. This framework allows incremental context loading and context compression, maximizing token efficiency while maintaining cross-session continuity.