AutoHarness Model Card
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AutoHarness Model Card
| Field | Value |
|---|---|
| Repository | aiming-lab/AutoHarness |
| Category | Automated Agent Harness Engineering Framework |
| Stars / forks snapshot | 295 / 23 |
| Language | Python |
| License | MIT |
| Raw capture | raw-github/aiming-lab_autoharness.md |
| Updated by | hourly public metadata update, 2026-05-30 19:17 +0800 |
1. Role in Self Evolve
AutoHarness provides a governance-oriented runtime that wraps LLM clients and upgrades demo agents into auditable, policy-controlled production loops. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.
2. Working Principle
treat harness as the controllable layer around model reasoning -> enforce multi-step governance and risk checks on tool execution -> track costs, logs, and sessions -> feed failures back into harness policies to improve future agent runs
3. Evidence Path
web-observed GitHub page showed 295 stars, 23 forks, 8 commits, MIT license, and the explicit principle ‘Agent = Model + Harness’ with multi-mode governance. Shell GitHub API access remained blocked by DNS and local gh auth was invalid, so this card treats the snapshot as web-observed rather than API-verified.
4. Teaching Use
Use this card to explain Automated Agent Harness Engineering Framework: it shows how harness/runtime/benchmark layers convert agent behavior into reproducible and auditable engineering workflows.
5. Limits
The repository was not cloned in this iteration; no benchmark run, plugin install, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.