A Governance-Layer Architecture for Human-Governed AI Workforces

This working paper specifies a governance-layer architecture for human-governed AI workforces: per-agent performance ledgers, earned resource budgets, structured request queues, HR-style evaluation, explicit lifecycle states, and first-class observability and tool registries. It is a vendor-, model-, and runtime-agnostic design specification with a pre-registered empirical validation roadmap, not a claim of demonstrated superiority.

Read the paper

• PDF (46 pp): download
• arXiv: submission in progress (cs.AI, cross-listed cs.SE) — the arXiv ID will be added here on publication.

Reference artifacts (code + data)

An open, inspectable reference implementation of the Governance Layer — PostgreSQL schemas, the Rule H10 reward-hacking detector with automated tests (5/5 passing, stdlib-only), HR-evaluator SQL, templates, a synthetic 5-agent × 12-week ledger, the companion architecture specification, and the failure-mode catalogue. All included data is synthetic.

• GitHub: github.com/tashfindelwar/ai-workforce-governance-layer (release v1.0-arxiv, MIT)
• Archival DOI (Zenodo): 10.5281/zenodo.21396612 (v1.0.1: 10.5281/zenodo.21396613)
• 30-second try: python3 scripts/rule_h10.py data/synthetic_agent_ledger.csv → flags exactly moderator-04.

Figures & reproducibility

Figure sources and the reproducibility checklist ship in the GitHub repo (docs/, examples/, data/README.md). The empirical study (Phase A ledger study, Phase B architecture comparison) is pre-registered on OSF: osf.io/kyabr. Results paper scheduled Q4 2026.

Cite

Delwar, T. (2026). A Governance-Layer Architecture for Human-Governed AI Workforces. Working paper. Artifacts: github.com/tashfindelwar/ai-workforce-governance-layer (DOI: 10.5281/zenodo.21396612).

Contact: tashfin@kotha.app