Notes on ai & automation

Practical notes on deciding where AI and automation genuinely help, and where people and systems need to stay in control.

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What ai & automation covers

AI and automation are most useful when they improve a specific workflow without making outcomes harder to understand or verify. This category covers responsible adoption, reviewable AI outputs, human oversight, and the engineering practices that keep AI-assisted work dependable.

Explore AI and automation

Questions explored here

Which business workflows are suitable for AI or automation?
How can a team keep AI-generated output reviewable and accountable?
What should teams check when AI writes more of the code?
Where should a human remain responsible for a workflow decision?

Current thinking on ai & automation

4 practical articles in this editorial category.

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AI & automation · 6 min read

What should an AI agent be allowed to access before it can act?

Define the records, tools and destinations an agent needs, then enforce those limits outside the model. Safe access starts before the first action.

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AI & automation · 9 min read

When should an AI agent act, and when should it only recommend?

An AI agent should earn authority through bounded, reversible, observable work. Let it recommend when the consequence or uncertainty is high, and automate action only where the controls can carry the risk.

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AI & automation · 8 min read

What should teams review when AI writes more of the code?

When code becomes cheaper to produce, the scarce work moves to deciding what should change, proving that it works, and keeping the product safe to operate.

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AI & automation · 9 min read

How can AI workflows help a business without making operations harder to control?

AI workflows can reduce repetitive knowledge work, speed up decisions, and make operations easier to scale, but only when AI is given a bounded role inside a process the business can still understand and control.

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