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.
A practical guide to how AI agents observe, decide, act, verify results, and stop safely while doing real work.
The self-improving company is the right idea. Without verifiers, ownership, and durable history, the loop drifts instead of improving.
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.
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.
A step-by-step operating model for running a coding agent through your existing tracker, repo, and review step, gated by planning and risk scoring.
Most work you hand an agent has no automatic pass or fail. Here is how to decide it is good enough without redoing it yourself.