Harnesses & tools

Stories

• What Is a Skill Worth? Structure-Aware Shapley Valuation of Agent Skills (arXiv)

  arXiv preprint · Story page

  SkillSV puts a value on the pieces inside an agent skill, separating what the content is worth from what the context costs.

• Microsoft's Orchard Beats Proprietary AI Agents at 10x Lower Cost (AlphaSignal)

  newsletter item · Story page

  A Kubernetes-native framework for training coding, browser, and productivity agents with small models.

• Building a C compiler with a team of parallel Claudes (Anthropic Engineering)

  Anthropic engineering post · Story page

  Sixteen parallel agents built a Rust C compiler able to compile the Linux kernel, with little active supervision.

• Agent threads that ping back form an implicit kanban of dependent work (swyx)

  X post · Story page

  Set one agent thread to ping back when it finishes and you get an implicit dependency graph of threads, each holding its own work and its own agents. swyx wants a real UI for it, which is roughly where multi-agent tooling is heading.

• How Cursor Router chooses the right model for the task (Cursor)

  Cursor blog · Story page

  Cursor's router is trained on performance against real developer work rather than benchmark scores, and picks a model from the current turn, recent conversation state, task category, and tool calls. Model routing is quietly becoming a harness responsibility rather than a settings toggle.

• Prime Intellect's Prime Agent Beats Human Experts on ARC-AGI-3 by Rewriting Itself (AlphaSignal)

  newsletter item · Story page

  Prime Intellect says its open-source Prime Agent reached 95.5% on ARC-AGI-3 by letting the model rewrite its own scaffolding at runtime. The number is the lab's own, on a benchmark the lab chose, so treat it as a demonstration that the loop runs rather than a settled score.

• Introducing Mods: Enabling Agents to Self-Improve through Harness-Level Adaptation (Letta)

  Letta blog · Story page

  Mods lets an agent extend and revise the Letta Code harness itself, not only its prompts, memory, or skills. It treats harness behavior as learnable state, building on Letta's versioned context store and self-editing memory tools.

• Harness design for long-running application development (Anthropic Engineering)

  Anthropic engineering post · Story page

  Generator-evaluator loops, task decomposition, and structured context handoffs, ending in a planner, generator, and evaluator setup.

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