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The Law Already Assumes Your AI-Run Company Needs a Human in Charge
Argentina's bill for AI-run companies still requires a human administrator liable for outcomes. What that means for anyone building with autonomous agents.
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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.
6 min 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.
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7 min read
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.
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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.
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7 min read
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.
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7 min read
Why reliable agent work depends on verification, context, approvals, and durable workflow history, not just better agent interfaces.
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6 min read
Why approvals, questions, failures, and exceptions need a shared inbox before agent work becomes trustworthy at company scale.
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Why boards help visibility but fail to provide the approvals, verification, history, and control recurring agent work actually needs.
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7 min read
How small teams can assign work to agents without losing review quality, context, ownership, or accountability.
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6 min read
Why saved prompts stop being enough once agent work repeats, and what a real repeatable workflow needs instead.
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6 min read
How to give agents real autonomy with explicit boundaries, approvals, verifiers, and escalation paths instead of vague trust.
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