MultiAgentEval Enterprise Harness Model Card
这是可索引项目报告证据页：它保留 MultiAgentEval Enterprise Harness Model Card 的源材料入口、机制线索和限制提醒；正文仍需 reader/editor 与 academic public-copy review 后才能当作最终结论引用。
MultiAgentEval Enterprise Harness Model Card
| Field | Value |
|---|---|
| Repository | najeed/ai-agent-eval-harness |
| Category | Enterprise Multi-Agent Evaluation and Verification Harness |
| Stars / forks snapshot | 28 / 6 |
| Language | Python |
| License | Apache-2.0 |
| Raw capture | raw-github/najeed_ai-agent-eval-harness.md |
| Updated by | hourly public metadata update, 2026-06-02 13:53 +0800 |
1. Role in Self Evolve
najeed/ai-agent-eval-harness is an enterprise-grade reliability framework for AI agents with benchmark, replay, and verification surfaces. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, independent benchmarks, and durable memory/runtime surfaces before claiming stable improvement.
2. Working Principle
simulate business workflows through benchmark scenarios and shims -> replay deep traces for verification and debugging -> compare agent reliability across environments and workflows -> close the agentic reliability gap with explicit eval infrastructure
3. Evidence Path
web-observed GitHub page showed 28 stars, 6 forks, 252 commits, Apache-2.0 license, and MultiAgentEval reliability-framework positioning. 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 Enterprise Multi-Agent Evaluation and Verification Harness: it shows how benchmark/harness or memory/runtime layers convert agent behavior into reproducible and auditable engineering workflows.
5. Limits
The repository was not cloned in this iteration; no benchmark run, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.