# Governed AI-Assisted Engineering: Graduated Human Oversight for Agentic Code Generation in Regulated Domains

arXiv: https://arxiv.org/abs/2606.22484 · HTML: https://mlsanity.com/paper/2606.22484/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 18 of its 51 references that are in the map.
- First posted on arXiv 21 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 18 of 18)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Magentic-UI: Towards Human-in-the-loop Agentic Systems](https://mlsanity.com/paper/2507.22358/) (2025)
- [Human-In-the-Loop Software Development Agents](https://mlsanity.com/paper/2411.12924/) (2024)
- [CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding](https://mlsanity.com/paper/2512.04111/) (2025)
- [Agentic AI in the Software Development Lifecycle: Architecture, Empirical Evidence, and the Reshaping of Software Engineering](https://mlsanity.com/paper/2604.26275/) (2026)
- [Towards Automated Governance: A DSL for Human-Agent Collaboration in Software Projects](https://mlsanity.com/paper/2510.14465/) (2025)
- [Rethinking Software Engineering for Agentic AI Systems](https://mlsanity.com/paper/2604.10599/) (2026)
- [Human-In-The-Loop Software Development Agents: Challenges and Future Directions](https://mlsanity.com/paper/2506.11009/) (2025)
- [Agentic AI in 6G Software Businesses: A Layered Maturity Model](https://mlsanity.com/paper/2508.03393/) (2025)
- [The Productivity-Reliability Paradox: Specification-Driven Governance for AI-Augmented Software Development](https://mlsanity.com/paper/2605.01160/) (2026)
- [Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap](https://mlsanity.com/paper/2605.04532/) (2026)
- [TDD Governance for Multi-Agent Code Generation via Prompt Engineering](https://mlsanity.com/paper/2604.26615/) (2026)
- [From Black-Box Confidence to Measurable Trust in Clinical AI: A Framework for Evidence, Supervision, and Staged Autonomy](https://mlsanity.com/paper/2604.26671/) (2026)
- [A Dual-Helix Governance Approach Towards Reliable Agentic Artificial Intelligence for WebGIS Development](https://mlsanity.com/paper/2603.04390/) (2026)
- [The AI Codebase Maturity Model: From Assisted Coding to Fully Autonomous Systems](https://mlsanity.com/paper/2604.09388/) (2026)
- [Structural Quality Gaps in Practitioner AI Governance Prompts: An Empirical Study Using a Five-Principle Evaluation Framework](https://mlsanity.com/paper/2604.21090/) (2026)

## Built on by (most cited first, 2 of 2)

- [Where Accountability Lives: Mapping Human Responsibility to Workflow Artifacts in Agentic Software Development](https://mlsanity.com/paper/2608.15678/) (2026)
- [From Traceability to Justifiability: Accountability Structures in Agentic Software Engineering](https://mlsanity.com/paper/2608.23610/) (2026)

## Abstract (arXiv)

The adoption of agentic AI coding systems -- where autonomous agents generate, review, test, and deploy code with minimal human intervention -- creates a governance challenge in regulated industries. Existing frameworks address AI-assisted development maturity or the productivity-reliability tension but offer no mechanism for calibrating human oversight intensity to regulatory impact. We present the Governed AI-Assisted Engineering (GAIE) framework, a three-tier graduated human oversight model for agentic code generation in regulated domains. GAIE introduces the Oversight Classification Model (OCM), a deterministic decision function that classifies code generation tasks by regulatory impact, customer proximity, reversibility, and data sensitivity to route them through one of three oversight tiers: human-in-the-loop (strategic functions), human-over-the-loop (customer-impacting), or automated-with-monitoring (internal). Each tier defines required evidence artifacts for compliance auditability. We map GAIE against the Bank of Thailand's 2025 AI risk-management policy and demonstrate cross-jurisdiction applicability to MAS (Singapore), NIST AI RMF, ISO/IEC 42001, and the EU AI Act. Evaluation through regulatory coverage analysis, comparative framework analysis, and analytical productivity modeling suggests that graduated oversight preserves 84--97% of agentic coding velocity (central estimate: 91%) while maintaining compliance evidence coverage for regulated functions. GAIE contributes a framework that explicitly bridges AI-assisted development maturity with regulatory governance through proportionate human oversight.