ck:context-engineering

To prevent context overload in long-running LLM sessions, you need structured context analysis to estimate utilization, detect degradation risk, and flag poisoning patterns before quality drops.

Lost-in-the-middle degradation occurs when an LLM ignores mid-context information; context compression fixes it by evaluating compaction quality and preserving critical facts during context resets.

Implement token budgeting for multi-agent workflows by applying deterministic scripts to monitor context-window pressure, enforce usage limits, and isolate context between agents before limits become a problem.

Use context isolation when separating multi-agent state in file-based memory systems, and use compression planning when you need to compare compaction quality and preserve critical facts within a single agent conversation.

Yes, this approach supports pytest for validating agent memory persistence by applying probe-based compression evaluation to verify that critical facts are preserved during context compaction.

Context health analysis limitations include relying on deterministic scripts for health scoring, which estimates degradation risk and flags poisoning patterns but cannot fully prevent lost-in-the-middle failures in complex agent workflows.