The original title is: "Splitting a Slow AI Agent Into Parallel Sub-Agents: Performance Results"

Short summary

The author solved slow, expensive AI agent performance by splitting a single bloated OpenClaw session into parallel sub-agents with fresh context windows. Latency dropped from 14 minutes to under 90 seconds, token use fell ~60%, and error rates on multi-step tasks dropped from 23% to 4%. The key rule: tasks requiring more than 10 files or 5 tool calls go to isolated sub-agents rather than the main session.

• •Splitting one agent into parallel sub-agents cut latency from 14 min to 90 sec and tokens by ~60%
• •Sub-agents run with fresh context, preventing history-induced confusion and hallucination
• •Rule of thumb: tasks needing 10+ files or 5+ tool calls should be delegated to sub-agents

Generated with AI, which can make mistakes.