Add a research-backed acceptance harness for template go service (template new microservices service-runtime proto)

[haasonsaas]

Summary

Convert the repo's latent product contract into a repeatable benchmark suite with explicit pass/fail evidence.

This issue was generated from an org-wide EvalOps mining pass on 2026-05-10 07:57 UTC. It combines live GitHub repo signals with a per-repo arXiv search. Treat the research links as grounding for a concrete implementation, not as a request for a literature review.

Repo Evidence

• Repository description: Template for new EvalOps Go microservices — service-runtime, proto, CI, GHCR publishing, CODEOWNERS
• Tree signals: 0 docs files, 2 workflows, 0 proto files, 0 test-like files.
• Anchor files sampled: README.md, go.mod

Research Grounding

Repo axes: infra, governance, security, evaluation

Search keywords: github, indirect, com, run, uses, actions, build, service-runtime, make, name, template, test

• arXiv:2604.04749v1 AI Trust OS -- A Continuous Governance Framework for Autonomous AI Observability and Zero-Trust Compliance in Enterprise Environments (Eranga Bandara, Asanga Gunaratna, Ross Gore, Abdul Rahman, Ravi Mukkamala, Sachin Shetty), 2026.
• arXiv:2604.26152v1 AI Observability for Large Language Model Systems: A Multi-Layer Analysis of Monitoring Approaches from Confidence Calibration to Infrastructure Tracing (Twinkll Sisodia), 2026.
• arXiv:2604.17092v1 AI Observability for Developer Productivity Tools: Bridging Cost Awareness and Code Quality (Happy Bhati, Twinkll Sisodia), 2026.
• arXiv:2604.03262v1 AI Governance Control Stack for Operational Stability: Achieving Hardened Governance in AI Systems (Horatio Morgan), 2026.
• arXiv:2001.07935v2 CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking (Grigori Fursin, Herve Guillou, Nicolas Essayan), 2020.
• arXiv:2502.15859v4 AI Governance InternationaL Evaluation Index (AGILE Index) 2024 (Yi Zeng, Enmeng Lu, Xin Guan, Cunqing Huangfu, Zizhe Ruan, Ammar Younas), 2025.
• arXiv:2510.11595v1 Reproducibility: The New Frontier in AI Governance (Israel Mason-Williams, Gabryel Mason-Williams), 2025.
• arXiv:2302.01061v1 MLOps with enhanced performance control and observability (Indradumna Banerjee, Dinesh Ghanta, Girish Nautiyal, Pradeep Sanchana, Prateek Katageri, Atin Modi), 2023.
• arXiv:2305.14865v1 A Game-Theoretic Framework for AI Governance (Na Zhang, Kun Yue, Chao Fang), 2023.
• arXiv:2603.26865v1 A federated architecture for sector-led AI governance: lessons from India (Avinash Agarwal, Manisha J. Nene), 2026.

What To Build

• Define the smallest representative template-go-service golden workflow and capture expected inputs, outputs, and evidence artifacts.
• Add fixtures for a successful path, an ambiguous/degraded path, and a failure path.
• Publish a command that local agents and CI can run before shipping related changes.

Acceptance Criteria

• A short design note names the repo-specific workflow, threat or correctness model, and the research assumptions being adopted.
• A runnable check, fixture, or verifier exercises the new contract in CI or an equivalent local command documented in the repo.
• The implementation emits or stores enough evidence for a downstream agent/operator to cite inputs, decisions, and outputs.
• At least one negative/degraded-mode case is covered so failures are observable rather than silently accepted.
• Documentation links the new behavior to the relevant EvalOps platform primitive or explicitly records why this repo remains standalone.

Notes

• Generated issue 1/5 for evalops/template-go-service by evalops_org_miner.py.
• Before implementation, confirm the sampled latent-spec snippets still match main; this issue intentionally cites exact file paths/lines where the mining pass saw them.