fafa.👩🏻‍💻

Uber shared the exact agent setup behind 70% of their pull requests. Weekly users grew 7x, and cost per session still fell 52%. Here's what you can copy. The setup: coding agents that run locally and in the cloud, used by engineers and non-engineers. They have 3,600+ shared skills and 30K+ skill runs a day. The main model plans and checks. Cheaper subagents do the heavy work. The savings came from defaults, not from a smarter model. What to copy: 1. Compact early Auto compaction at 400K tokens, even on 1M context models. 2. Lower the default effort Reasoning effort is set to Medium. Turn it up only when a task needs it. 3. Cheaper subagents The main model plans. Subagents on a cheaper model do the reading and searching. 4. Longer cache for the main session Prompt cache lasts 1 hour for the main session, 5 minutes for subagents. 5. Don't load every MCP tool With 100+ tools installed, the schemas alone cost 50K to 70K tokens before the first prompt. Call tools through a CLI and search for them only when needed. 6. Batch tool calls into one script That cut tokens by 55% to 71%, even on small queries. Start with one default today: compaction, effort level or subagent model.