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Writing: AI / Systems

AI / Systems: Writing on artificial intelligence, decision-making and the engineering that turns models into useful, understandable systems.

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Program state enters a decision model; a typed result and its uncertainty pass to a policy in code before an action is taken.

When software needs a decision, not another paragraph

A billing message needs the right destination. TypeSafe’s Jev offers one way to turn an interpretation into a result that software can act on.

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  1. A model proposes an action, the harness checks authority, and the environment returns evidence for the next step.

    When an AI agent says “done”, what should we check?

    A convincing completion message is only the beginning. Harness engineering is the work of helping an agent act, check the result and recover when needed.

  2. A wheeled inspection robot with a local compute enclosure stands beside industrial pipes; a service laptop and loose network cable sit nearby.

    The connection drops. The robot is still there.

    A closed door cuts a robot off from the cloud. Following one inspection explains why small models, local decisions and a shared memory of the job matter.

  3. Uncrewed assurance console showing decision evidence and authority state

    Why I keep the planner classical

    A quantum component can help with a calculation without taking over the decision. Keeping that distinction clear makes QANTIS easier to test and use.

  4. A maintenance worker and an inspection robot beside a damp-looking floor patch in a service corridor.

    Someone still has to go and look

    A robot reports a possible wet floor. Following that finding to the maintenance team reveals the everyday problem behind the NODERIQ research programme.

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