Advanced strategies for managing AI agent context windows, covering context rot prevention, multi-agent coordination, and action space optimization. Key techniques include context compaction (reversible, strips redundant info) vs. summarization (lossy, triggered at token thresholds), applying Go's concurrency principle to multi-agent context sharing, and maintaining a small hierarchical toolset (~20 core tools) to avoid context confusion. The 'Agent-as-a-Tool' pattern is recommended over org-chart-style agent hierarchies, treating sub-agents like deterministic functions with structured output schemas. Practical tips include defining pre-rot thresholds, avoiding dynamic RAG for tool definitions, and embracing iterative harness rewrites as models improve. The core insight from Manus: biggest performance gains came from removing complexity, not adding it.
