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.
Safe organizational learning turns execution evidence into versioned, human-approved workflow changes with post-change evaluation.
Reliable agent action depends on a current internal model, versioned policy, scoped memory, and evidence after execution.
Ashby's law explains why reliable AI operations need roles, tools, constraints, and escalation instead of one general agent.
Stafford Beer's Viable System Model clarifies operations, coordination, control, audit, intelligence, and policy for human-agent teams.
How small agencies use AI agents to prepare recurring client work while preserving review quality, context boundaries, and trust.
A practical way for indie hackers to delegate recurring marketing, support, research, and operations while keeping control.
A production-readiness model for AI agents covering state, retries, permissions, verification, observability, and recovery.
A practical guide to how AI agents observe, decide, act, verify results, and stop safely while doing real work.
A decision framework for delegating software development tasks based on clarity, verification, blast radius, and reversibility.
The self-improving company is the right idea. Without verifiers, ownership, and durable history, the loop drifts instead of improving.
Sam Altman's friends are betting on a company with one employee. Here's what an actual week running one looks like.
Workers spend hours a week botsitting AI agents: feeding context, catching mistakes, cleaning up. Autonomy levels and one inbox fix why.