Author
Antoine Buteau

Antoine Buteau

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Automation series #10: The Automation Architecture Worksheet

The point of automation architecture is not to produce diagrams. The point is to make better decisions before the workflow is in production, touching customers, writing to systems, and creating trust problems. Use this worksheet before building or expanding an AI automation workflow. It is deliberately practical. If a team
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Automation series #9: Failure Modes, Security, and Blast Radius

Failures will happen. The useful question is not whether you can prevent every failure. You cannot. The useful question is how much damage a failure can do, how quickly you can detect it, and how cleanly you can recover. That is blast radius. AI automation makes blast radius more important
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Automation series #8: Agents Inside Bounded Workflows

Agents are useful. They are also easy to over-romanticize. The wrong mental model is an autonomous digital employee roaming across systems, figuring things out, and taking initiative. That makes for an exciting prototype and a weak control environment. The better model is simpler: an agent is a constrained operator
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Automation series #7: Observability, Auditability, and Replay

If you cannot explain what your automation did, you do not have automation. You have a liability. This becomes painfully obvious after the first failure. A customer received the wrong email. A ticket was routed to the wrong team. A contract field was extracted incorrectly. A workflow retried an action
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Automation series #6: State, Idempotency, Retries, and Queues

The least glamorous parts of automation are usually the parts that decide whether it works. State. Idempotency. Dedupe. Retries. Queues. Exception paths. These are not backend trivia. They are the difference between a system that can be trusted and a system that occasionally does something weird and nobody knows why.
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Automation series #5: Human-in-the-Loop Is a Design Pattern, Not a Failure

Teams often treat human review as an admission that automation did not work. That is backwards. Human review is how you make automation safe enough for consequential work. It lets the system move fast on routine cases, slow down on risky cases, and learn from the edge cases that would
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Automation series #4: The Automation Boundary: Code vs Model vs Human

Every automation has a boundary problem. What should code decide? What should a model decide? What should a human decide? If you do not answer that explicitly, the boundary gets decided accidentally. Usually by whoever wrote the first prompt, connected the first API, or approved the first demo. That is
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Automation series #3: AI Automation: When Judgment, Language, or Ambiguity Matters

AI automation earns its keep when the work is ambiguous. Ambiguous, not mystical. The customer describes a billing issue without using the word billing. A contract contains a renewal clause buried in legal language. A sales call has five objections, but only two matter. A support ticket could be a

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