latent.fs / tags / Agent — 11 items

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#Agent

2026-10 4 posts

  1. In the Agent Era, the Scope a Specialist Software Supplier Can Choose

    Part 4 of 4: lock agent features in one dedicated agent, or expose them as tools and work assets any worker can use? It shapes what a supplier can sell.

    AI·Agent #39

  2. Seeds and Germination: Software That Evolves in the Field

    Part 3 of 4: fitting a product to each customer site with people and agents, building runtime adaptation into it, and feeding field experience back.

    AI·Agent #38

  3. Every Organization Needs an Agent Runtime

    Part 2 of 4: why even air-gapped organizations will run a general-purpose agent runtime on work PCs, and how to connect it to internal models and tools.

    AI·Agent #37

  4. Separating the Worker from the Tool

    Part 1 of 4: work spans products, so general-purpose agents become workers and products their tools. What to expose, and where software keeps its value.

    AI·Agent #36

2026-09 2 posts

  1. Can tgrep, CodeGraph, Mantic, or ripwire Replace ripgrep?

    tgrep, CodeGraph, Mantic, and ripwire vs ripgrep on eight repos. Each hinges on a different variable: query, language coverage, file names, or phrasing.

    AI·Agent #35

  2. ego-browser and Aside Fail Differently

    Same missions, two browser agents: ego-browser isolates its tabs and returned success on missed clicks; Aside works in your real tabs and threw errors.

    AI·Agent #34

2026-05 5 posts

  1. Requirements as Latent State

    A requirement is a latent state and the user's sentence a noisy observation. What follows for EARS, spec-driven development, harnesses, and when to ask.

    AI·Agent #30

  2. Skill and Harness

    A skill compresses how to behave; a harness organizes how to tell if that behavior nears the goal. Why the two still contain each other in practice.

    AI·Agent #29

  3. Harness as Environment

    A vector DB full of experience does not make an agent adapt. That takes a harness: what may change, how success is judged, how far a failure rolls back.

    AI·Agent #28

  4. Memory as Adaptation

    Long-running agents need more than a RAG store: why memory split into working, episodic, semantic, and procedural layers that ground policy adaptation.

    AI·Agent #27

  5. Weights, Prompts, Codes as Parameters

    If agent problems are POMDPs, weights, prompts, and code are parameters at three layers of one policy space. Why the layers opened in that order.

    AI·Agent #26

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