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A person who works with AI to produce quality, scalable output that helps humans or other agents get something done.
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Provenance is knowing who wrote what, when, based on what input, and whether it is authoritative.
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Shared state is the canonical surface where humans and agents both read and write the same plan, tasks, drafts, and decisions.
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AI-powered distribution is the collapse of every promotion channel into something one builder can run.
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The agent graduation path is the four-step progression every task takes: manual, co-piloted, system-to-build, autonomous.
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An agent context protocol is the structured contract that lets one agent or tool hand context to another agent without a human...
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The Builder Weekly
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Foundation content

Foundation content on AI building, AI economics, and trust in AI systems. Each article answers a canonical question and serves as a reference point for related weeklies, monthlies, and tutorials.

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  • What is provenance in agent systems?

    Provenance is knowing who wrote what, when, based on what input, and whether it is authoritative. Every write needs those four answers. Without them, shared state becomes shared ambiguity. Provenance is the bridge between shared state and shared trust.

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  • What are the risks of AI building?

    The risks of AI building fall into six categories. Each has a known failure mode, a known mitigation, and a severity that maps to the stakes of the output. Managing them is the builder's job, not the model's.

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  • What is AI accountability?

    AI accountability is the engineered ability to trace a wrong output back to a cause, assign responsibility for fixing it, and prevent recurrence. The agent is not accountable. The builder is.

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  • What is human-in-the-loop?

    Human-in-the-loop is the design choice of placing a human at specific points in an otherwise-automated workflow to approve, verify, or redirect output. Right placement is where cost of error is high and cost of attention is low.

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  • What is outcome verification?

    Outcome verification is the practice of confirming that an agent's action achieved the intended effect, not just that the action executed. It is the difference between the email was sent and the email led to a meeting.

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  • What is trust in AI systems?

    Trust in AI is not about believing AI is correct. It is about building verification systems that catch when it is not. Trust is an engineering problem, not a faith problem.

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