Getting More Out of Every AI Token
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Engineers at monday.com share three practical strategies for reducing LLM token usage and AI costs in agentic workflows. The Caveman plugin instructs models to respond tersely, cutting ~65% of output tokens. RTK is a proxy that compresses shell command output (e.g., docker ps) by 60–90% before it reaches the model. Headroom is a broader context optimization layer that compresses tool outputs, logs, files, RAG chunks, and conversation history by 60–95%, with a reversible Compress-Cache-Retrieve mechanism so the agent can fetch full details when needed. The three tools target different boundaries and can be stacked: Headroom wraps the agent, RTK handles shell output underneath, and Caveman steers model verbosity. The post also cautions that over-compression can degrade answer quality, so token graphs and answer quality should both be monitored.
