#governance

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AI vendor lock-in — when the model is not the only dependency

AI lock-in is not created only by choosing a model. It grows through contracts, data, tools, evals, pricing and workflows that slowly stop being portable.

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Agent infrastructure — the boring layer agents need to work

An agent is not just a model with a task. In production it needs identity, permissions, inboxes, tools, memory, audit, telemetry and clear boundaries. Without infrastructure, autonomy is just a pretty demo with risk attached.

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Frontier model governance — who checks the model before release

Frontier model governance asks who tests the strongest models before deployment, under which rules and with what power to intervene. A voluntary audit, a system card and government testing are not the same thing.

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Agent context contracts — local rules the model must not guess

A context contract states a project’s local rules: what an agent may change, how it should proceed, how to verify a change, and when to hand work to a person. It limits guesswork where a general prompt is insufficient.

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Agent memory — what an AI system may carry from past runs

Agent memory is not a bigger prompt. It is a design for what gets stored, how it is retrieved, when it may be used, and who can correct a bad memory. Good memory saves work; bad memory produces confident mistakes with a history.