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.
Argentina's bill for AI-run companies still requires a human administrator liable for outcomes. What that means for anyone building with autonomous agents.
When an agent's work fails a check, it needs an owner, the evidence, and a next action. That is inbox work, not another alert to scroll past.
Autonomous agents can run up real costs through retry loops and runaway work. Set budgets, retry limits, and stop conditions before the spend, not after.
The same work comes back every week. Here is an operating model for turning recurring jobs into repeatable workflows you can hand off and trust.
Why reliable agent work depends on verification, context, approvals, and durable workflow history more than on better agent interfaces.
Why approvals, questions, failures, and exceptions need a shared inbox before agent work becomes trustworthy at company scale.
Why boards help visibility but fail to provide the approvals, verification, history, and control recurring agent work needs.
How small teams can assign work to agents without losing review quality, context, ownership, or accountability.
Why saved prompts stop being enough once agent work repeats, and what a real repeatable workflow needs instead.
How to give agents real autonomy with explicit boundaries, approvals, verifiers, and escalation paths instead of vague trust.