# Model-Based Agentic Software Engineering

arXiv: https://arxiv.org/abs/2608.25174 · HTML: https://mlsanity.com/paper/2608.25174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 9 of 9)

- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Runtime Governance for AI Agents: Policies on Paths](https://mlsanity.com/paper/2603.16586/) (2026)
- [From Governance Norms to Enforceable Controls: A Layered Translation Method for Runtime Guardrails in Agentic AI](https://mlsanity.com/paper/2604.05229/) (2026)
- [SysLLMatic: Large Language Models are Software System Optimizers](https://mlsanity.com/paper/2506.01249/) (2025)
- [Towards Automated Governance: A DSL for Human-Agent Collaboration in Software Projects](https://mlsanity.com/paper/2510.14465/) (2025)
- [How Do Agents Perform Code Optimization? An Empirical Study](https://mlsanity.com/paper/2512.21757/) (2025)
- [The End of Code Review: Coding Agents Supersede Human Inspection](https://mlsanity.com/paper/2606.13175/) (2026)

## Abstract (arXiv)

Coding agents increase implementation capacity without automatically making project intent, system structure, or acceptance evidence explicit. As implementation becomes abundant relative to engineering judgment, the scarce work shifts toward choosing useful abstractions, producing evidence, and determining which obligations govern acceptance. Existing workflows address parts of this gap through larger prompts, repository retrieval, or perchange review, but still require agents and engineers to reconstruct consequential properties. As an alternative, we present Model-Based Agentic Software Engineering (MAGE). MAGE is a framework and a theory for building trustworthy autonomy from commodity intelligence. MAGE addresses a representation problem and an authority problem: it externalizes the smallest purposeful representation needed to answer an engineering question, then gives settled obligations proportionate authority through constraints, sensors, validators, and gates. It keeps uncertain intent open and turns recurring reconstruction and judgment into durable engineering structure that later work can inherit. We developed MAGE from a longitudinal case and refined it through six independently reported industrial accounts. Across these sources, MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.