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07 / Insights

From the practice.

Notes and papers on the gap between learning about AI and putting it into production.

Whitepapers

  1. Linear Horizon whitepaper/

    The product as its own expert

    Give each product an MCP server so an agent can inspect its code, telemetry, usage and history — then diagnose problems, cite evidence and propose the next improvement.

  2. Linear Horizon whitepaper/

    The four stages of AI adoption in the workplace

    A practical four-stage model for AI adoption — Assist, Standardise, Connect and Transform — and how to progress without treating autonomy as the goal.

  3. Linear Horizon whitepaper/

    How Silicon Valley is approaching fully agentic workflows

    Leading technology companies are not making entire organisations autonomous. They are making particular workflows agentic — with a defined objective, connected context, bounded authority and deliberate human control.

Notes

  1. What is an agent? What does it mean to be agentic?

    An agent is an LLM, plus a prompt, plus tools — placed inside a loop that lets it pursue a goal. To be agentic is to give a system limited discretion over how that goal is achieved.

  2. The gap between learning about AI and putting it into production

    Training without a path into the system leaves organisations fluent and stuck. The work is to close that gap.

  3. Where AI is actually useful — and where it is not

    The useful question is not what a model can do in general. It is what it can do here, against this data, these interfaces, and this risk.