Pattern: Token Compression Is Splitting Into Two Distinct Disciplines — Input-Side and Output-Side

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Two of the fastest-growing agent tools right now attack opposite halves of the context bill: Headroom compresses what enters the model (tool results, logs, RAG chunks) via AST- and schema-aware compressors, while Caveman compresses what leaves it (narration) via prompt-level style constraints. Their own benchmarks show the split matters — output-side compression collapses to single digits on agentic coding runs, while input-side compression is where the 60–95% reductions live. The practical rule: if your agent loop is tool-heavy, spend your effort on the input side; output-side tricks are for chat-shaped work.