Cut my browser-agent cost 50x by NOT using an agent loop. Plan-then-execute + numbers.

Been building a browser-automation layer for AI agents (think: sign up for SaaS, fill forms, pull OTPs, click verification links). The default playbook is the browser-use / Stagehand pattern: hand the LLM the page, let it pick the next action, repeat. Standard agent loop. Numbers I was seeing: - 20 to 50 LLM calls per task - $0.50 to $3.00 per task at Claude Sonnet 4.6 prices - Half the runs drifted off-task halfway through The thing nobody says out loud: most agent browser goals are LINEAR. "Go to notion.so, sign up with this email, paste the OTP." The LLM is great at sketching that plan ONCE. It is terrible at re-deriving it at every single step. So I flipped it: 1. One Anthropic Messages call: goal to JSON step list 2. Executor runs each step deterministically against Steel Chromium 3. Zero LLM calls during execution Step vocabulary is 10 verbs: navigate, click, fill, wait_seconds, wait_for_text, extract_text, wait_for_email, use_otp_from_inbox, open_link_from_inbox, done The last three are interesting. They read from the bound inbox in the same runtime, so the agent that owns the email is the same one driving the browser. No glue code between them. Numbers after the switch: - 1 LLM call per task - $0.01 to $0.05 per task - Way fewer drift failures (the executor throws on missing elements instead of hallucinating its way through) The tradeoff: if a page changes mid-flow, the run dies instead of replanning. For brittle long-running goals you still want a step-level loop. For the bulk of agent work (signups, verifications, form fills, navigation) the cheap version wins by an order of magnitude. Happy to walk through the planner prompt + step JSON schema if anyone's working on similar. What patterns have worked for you?