Multiplayer Harness for Agents and Humans

ThruWire has launched a multiplayer harness designed to unify human and AI agents as collaborative teammates on evolving work. The platform uses a graph-based notebook structure where changes propagate through dependencies, allowing teams to maintain coherence as strategies shift and new information emerges. Unlike traditional coding harnesses focused on static outputs, ThruWire targets subjective, evolving work like strategy and planning by making reasoning and intermediate artifacts explicit and persistent.

Benefit 01 Reusable building blocks Author once, compose anywhere. Work compounds across projects. ThruWire is an extensible multiplayer harness for unifying agents and humans as teammates. Build and execute shared structure that compounds. Most AI systems are built around getting to the right answer or the right code. Real work doesn't behave that way, especially when "right" is subjective and shifting. Thinking evolves. Assumptions change. New information appears. Priorities shift. What felt correct an hour ago quietly becomes incomplete or misaligned. At ThruWire, we measure success not by the output of any one agentic loop, but by the ability for the work to remain grounded over time as the ground shifts. Every piece of work should remain connected to what informed it, what depends on it, and how it can evolve. Changes should propagate through a structure that understands its own dependencies. As more humans and agents contribute, that structure is critical. Knowledge bases and LLM wikis capture knowledge, instructions, and playbooks, but they rarely capture the work needed to maintain grounding across many agentic loops. The reasoning, intermediate artifacts, and steps that produced an output are lost, making it harder to update without drift. ThruWire aims to make that implicit layer explicit. Our harness maintains structure across agentic loops and over time. Traditional harnesses are shaped for tasks that ends, like coding. ThruWire is shaped for work that evolves. Ideas can be revised, dependencies are visible, and changes propagate through everything that depends on them. Side by side Primary use cases Traditional harness Coding, chat-based workflows ThruWire Strategy, planning, multi-agent work Why it works or doesn't Traditional harness Clear structure ASTs , constraints, human feedback, and tests define correctness. ThruWire No formal structure or tests; correctness is subjective and shifting Core goal Traditional harness Arrive at a "right" answer ThruWire Maintain coherence as ideas change Mental model Traditional harness Outputs are final ThruWire Outputs are provisional Change handling Traditional harness Manual edits, localized fixes ThruWire Changes propagate through dependencies System behavior Traditional harness Static snapshots ThruWire Living, updating structure Every node in the graph is expressed as a notebook with a goal, steps, inline references, and artifacts—written in natural language so both humans and agents can read and edit it. Editing the prose is editing the structure. There is no separation between writing and authoring; both happen on the same surface. Each step produces intermediate artifacts, and each block produces a final artifact, all preserved and inspectable. Notebooks reference other notebooks and their artifacts. As blocks are added, the graph grows in dependency order—each one knows what it relies on and what relies on it. The structure is executable. When a notebook updates, downstream notebooks and their artifacts regenerate automatically. The whole graph stays consistent. The graph is the team's persistent reasoning structure. Humans, agents, and external tools all read from and write to the same surface, contributing both work and artifacts. When strategy shifts, the change ripples through dependencies and updates downstream artifacts. When new intelligence arrives, the graph adapts. Work compounds across people and time. ThruWire is for teams who feel the power of AI but can't quite harness it toward their objectives. It gives that work durable structure, visible dependencies, and a shared surface where humans and agents can keep building without losing the thread. Benefit 01 Author once, compose anywhere. Work compounds across projects. Benefit 02 Change one thing, everything downstream stays aligned. Benefit 03 Every output knows its inputs and how it was made. Benefit 04 Multiplayer authorship without losing coherence. Explicit dependencies make the graph legible. Isolation boundaries keep blocks clean. Execution identity makes reuse safe. Inspectable artifacts keep each step reviewable by humans and AI. 01 A block cannot consume an artifact it did not declare. The edges are written, readable, and enforced at runtime. 02 A block cannot see upstream artifacts it did not declare. Research that cannot see the product docs is research, not reverse-engineering. 03 Every block execution has a structural fingerprint. Same fingerprint, same result, safely cached and reused. Change one block, the system knows precisely which downstream blocks need to re-run. 04 Every step and block produces an intermediate artifact that humans and AI can review before downstream work continues. Intermediate work stays preserved for inspection and optimization without leaking into downstream context. ThruWire is a self-funded initiative by brothers Seth Rosen and Josh Rosen, based in the Boston area and exploring the future of multiplayer systems for work that compounds across humans and agents. ThruWire is our flagship concept and first product: the multiplayer harness. The common thread in our work is a belief that multi-agent and multi-human systems should become more structured, legible, and able to compound. Company Facts Tell us what kind of work you're trying to keep coherent.