Developer Cuts AI Agent Latency 80% by Splitting One Overloaded Session Into Three

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A developer found their OpenClaw AI agent slowing from 3 to 14 minutes per task after months of piling work into a single session, causing it to re-read up to 40,000 tokens on every turn. The root cause was architectural: a bloated context window was driving up latency, cost, and hallucinations within the same session. The fix involved spawning three parallel sub-agents, each with a fresh, isolated context window, to handle discrete tasks independently before returning only their results to the main agent. After the change, average task latency fell from 11.3 minutes to 2.1 minutes, and token use per research task dropped by roughly 58%. The developer now routes any task requiring more than 10 files or five tool calls to a sub-agent, reserving the main session only for work that needs continuous conversation history.

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