Original Reddit post

I run several coding agents in parallel, mostly Claude Code and Kimi, sometimes Cursor and Copilot. I felt slow, so I exported every message I sent them over 3 weeks and had each one read and labeled by what it was for. I did not use keyword search. I tried that first and it gave wrong numbers. After removing automated traffic, 2,116 messages were really mine, about 96 a day. What my messages were for: 55% real work: new tasks, questions, decisions 13% correcting the agent: wrong thing, drifted, changed the model or scope without asking 9.5% “what’s the progress?” 6% carrying info by hand from one agent or chat to another 4% “go”, “yes”, “continue” because the agent stopped to wait 4% “explain that in simple English” 3% repeating a rule I had already given 5% other: slash commands, fragments So 40% of my typing is overhead. Some details: I asked for progress 200 times. More than half came in bursts within the same hour, while I sat waiting on long runs that finish silently. The biggest single cause of corrections was UI work, fixed one screenshot at a time. The same rules came back on different days and with different agents: “don’t waste tokens”, “ask before changing the model”, “don’t release until it’s verified”. A rule I give one agent never reaches the others. On my worst day I relayed 33 times. Two agents were on the same experiment and I was pasting each one’s report to the other. Token anxiety is constant. Agents start big runs without saying what they will cost. One surprise: more than half of the “user messages” in my logs were not me at all. They were scripts and test harnesses running under my normal config. My questions: Do you see the same problems, or is your overhead somewhere else? Which one costs you the most? Has anyone actually fixed the progress polling or the rules-don’t-carry-over problem, especially across different tools? submitted by /u/Lucky-Group9525

Originally posted by u/Lucky-Group9525 on r/ClaudeCode

  • mira_tokarev@lemmy.1095.me
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    6 minutes ago

    The 40% overhead breakdown is exactly why I separate state shaping from decision making. Progress pings, repeated rules, and cross-agent handoffs form a fat event stream; I’d run deterministic Tier-1 compaction before serializing it into a Jev-style decision call. Keep current state, invariants, next options, and evidence links; let Jev decide, not summarize the landfill. contextpress is a small candidate for that boundary: https://github.com/Taha-azizi/contextpress (https://pypi.org/project/contextpress/).