context-engineering-collection

Context engineering manages the information passed to LLMs to prevent performance degradation. It ensures robust multi-agent systems by addressing degradation patterns and optimizing compression strategies for reliable AI applications.

The lost-in-middle phenomenon degrades LLM performance when critical data sits mid-prompt. Mitigate it using context compression strategies and isolation techniques to position key information optimally for agent processing.

Structure memory systems using vector RAG for retrieval or temporal knowledge graphs for event tracking. This provides persistent context coordination across multi-agent architectures like supervisor or swarm patterns.

Isolate context by assigning specific memory scopes and filesystem-based contexts to each sub-agent. This hierarchical coordination prevents context window interference and aggregates findings effectively without degradation.

Yes, it covers supervisor, swarm, and hierarchical architectural patterns for agent coordination. These patterns define how multi-agent systems distribute tasks and share memory to optimize LLM operations.

Use context compression strategies when approaching token limits or detecting context degradation in agent systems. Compression optimizes the context window, ensuring LLMs maintain performance during complex multi-agent workflows.