Every team shipping a coding agent — Claude Code, Codex, Cursor — is really running a serving-systems problem. The "tech behind the tech" is the LLM-serving stack underneath, and until now nobody had real data on what that workload looks like. New arXiv (2606.30560) from @bariskasikci's SyFI lab (@UWSyFi, @uwcse) is the first large cross-provider trace of real coding-agent use: ~4,300 sessions, 350K LLM steps, 430K tool calls, 43 developers, 8 months, Claude Code + Codex. It breaks the intuition that agents mean long generations. The median step replays ~119K context tokens to emit just ~214 output tokens — two orders of magnitude more reading than writing. So the bill is the context, not the answer: prefix tokens are 59.5% of total cost. Tool calls are brutally long-tailed: 80+ tools, but the top 3 are 80%+ of calls, and the 4% of calls that run >1 min eat 85% of all tool time. And the prefix cache everyone leans on? 95.7% hit rate — yet misses cluster right after a human pauses to think, amplifying prefill 3.8x. Those human-gap misses alone are ~46% of fresh tokens and ~13% of spend. For technical leaders: your agent's cost and latency live in the loop, the replayed context, and the idle gaps — not raw token generation. Tune tool-call overhead, append-length-aware prefill, and KV-cache eviction around human gaps before you scale the fleet.