LongHorizon-Harness
A harness from AMAP-ML (alibaba‘s mapping/ML group) for running agents across desktop apps and the command line — MIT-licensed, and explicitly a layer above an existing agent backend rather than an agent of its own. It plugs into claude-code, Codex and OpenClaw, and its published results run on Qwen. Routed here 2026-08-05 from Telegram. Its pitch is duration — “work across desktop apps and the command line for dozens of hours.”
Its framing sentence is the sharpest statement of this wiki’s founding bet that the corpus holds:
The model determines what an agent can do in one round. LongHorizon-Harness determines whether that work can be verified, preserved, and continued until the task is actually complete.
Three roles, and the boundary between them is the point
The design splits the loop into Manager, Executor and Auditor. The Manager keeps the goal, the verified progress so far, and the next step. The Executor does one focused task per round, and gets a fresh context each time. The Auditor checks the result independently, and only what it passes enters persistent state.
Most of the agent-loops-verification thread treats verification as something bolted onto the end of a loop. Here it is the write gate on state. An unverified result does not merely get flagged, it never becomes part of what the next round builds on — which is how the design claims to stop drift accumulating over dozens of hours. Compare statewright, which restricts which tools a phase may call, and agent-guardrails, which restricts what damage a call may do. This restricts what is allowed to become true.
The fresh-context-per-round choice puts it against the grain of the long-context direction: the Executor is deliberately denied the history, and the Manager’s distilled state is the only channel by which anything survives. Read against context-rot, that is the architectural answer to intrinsic rot rather than a mitigation of it — if the context never grows, the budget never erodes. The cost is that everything the Manager fails to write down is gone, which the design pays for with the Auditor.
The numbers, and exactly what they measure
All three results hold the backbone fixed at Qwen 3.7-Plus and switch the harness on:
| Benchmark | Tasks | Baseline | With harness | Gain |
|---|---|---|---|---|
| WeaveBench | 114 | 51.8% | 80.7% | +28.9 pts |
| OSWorld 2.0 | 108 | 2.8% | 8.3% | 3.0× |
| Terminal-Bench 2.1 | not stated | 69.7% | 77.2% | +7.5 pts, 24% fewer tokens |
Coverage is claimed across 13+ task domains — web, data analysis, debugging, design, documents, CAD, healthcare forms.
Two things are worth separating here, because the spoke has been burned by running them together.
What is new: the task counts are stated. 114 and 108 are named, which is more than agentsys has ever offered and more than statewright‘s 5-task SWE-bench subset. The Terminal-Bench row is the weakest of the three: no N, and the smallest gain.
The OSWorld figure is the one that should set a reader’s expectations, because 2.8% → 8.3% is a 3× improvement on a number that is still terrible. A project selling its own harness had every reason to lead with WeaveBench’s +28.9 and bury that row. It didn’t.
What is not new: this is self-reported, and it varies the wrong thing for our purposes. The measurements are the project’s own, with no third party in the loop, no trial count per task, and no described rubric. And structurally the design holds the model fixed and varies the harness — the identical shape as cc-thinking-skills, jetbrains-ponytail-tested and context-files-ablation-paper before it.
Why it does not close growth edge #2 — and what it does do
The spoke’s second growth edge wants a comparison that varies model and harness in the same design, because that is the only shape that can test agentsys‘s “Sonnet + harness beats raw Opus.” This is the seventh measurement to go past that question without asking it. The edge stands.
What it does supply is a magnitude for one arm of the substitution the edge is about. If a harness is worth +28.9 points on a fixed mid-tier backbone, then the question of whether that exceeds the gap between two model tiers is now a comparison between two numbers rather than between a number and a README claim. Someone still has to run the fourth cell. But the edge is better posed than it was yesterday, and it is worth recording that the harness arm keeps coming back large: statewright 2/10 → 10/10, this at +28.9 points, agentsys claiming 73–83% cost savings.
The caution that applies to all three is the same and it is not a small one: every one of these numbers was produced by the people selling the harness. The corpus has no independent replication of any of them.