Why passing raw execution logs into LLM context windows causes prompt degradation in complex workflows—and how persistent workspace scoping preserves model attention

When building autonomous LLM applications that execute multi-step code or file operations, appending complete raw tool outputs, API responses, and intermediate state directly into the primary prompt context rapidly pollutes the model's attention window. As context tokens accumulate across long runs, frontier models suffer from attention needle-in-a-haystack degradation, leading to instruction drift, hallucinated variable names, and costly token re-computation. Through benchmark testing across multi-tool developer workflows, we found that isolating execution state—saving raw outputs, browser captures, and generated artifacts to a persistent workspace environment while only returning concise, structured summaries to the orchestrator—reduces context bloat by over 70% while maintaining reasoning fidelity across multi-turn sessions. Full disclosure: I'm on the team building Spaces, where we implemented workspace-level context scoping to solve this exact bottleneck, and I'd love to hear how other LLM developers are handling context compaction and state persistence in production.