Botsitting Eats the Hours Agents Were Meant to Save
Workers spend hours a week botsitting AI agents: feeding context, catching mistakes, cleaning up. Autonomy levels and one inbox fix why.
Practical posts on turning recurring work into controlled Task Machine workflows with approvals, checks, agents on your own machines, and a history you can read.
Workers spend hours a week botsitting AI agents: feeding context, catching mistakes, cleaning up. Autonomy levels and one inbox fix why.
Argentina's bill for AI-run companies still requires a human administrator liable for outcomes. What that means for anyone building with autonomous agents.
A loop ran overnight and the bill arrived later. Hard budget caps, 80%/100% alerts, and ask-first-for-more keep agent spend bounded.
Agent work looks finished but you redo it anyway. Verifier checks, approvals, and step-level history turn plausible output into reviewable evidence.
Codex here, Claude Code there, decisions lost in scrollback. One shared inbox and durable tasks give agent work a real system of record.
An agent acted before you saw it, and trust is gone. Approval-first control routes every consequential action to one inbox before it ships.
Outreach, content, reports, and follow-ups keep sliding to next week. The fix is structural: recurring workflows agents run and you approve.
Most AI company tools assume you are starting from a prompt. Real businesses already run. The better model plugs agents into the stack you have.
A cron job that runs an agent prompt nightly fires blind. Recurring agent work needs a controlled loop, not a scheduler.
Two models for letting AI run your business: a company that runs itself, or an operating layer you direct. The honest tradeoffs.
Stop asking which AI agent platform is best. Use the axes that decide it for a solo founder, then map each category to where it fits.
Running a company with AI works as a controlled operating model, not a black box. Here is the model: workflows, one inbox, autonomy levels, budgets, verifiers.