# ARCHER: Agentic Rule and Compliance Harness for Executable Regulations

arXiv: https://arxiv.org/abs/2607.25566 · HTML: https://mlsanity.com/paper/2607.25566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 28 Jul 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, 13 of 13)

- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [Executable Code Actions Elicit Better LLM Agents](https://mlsanity.com/paper/2402.01030/) (2024)
- [Large Language Model-Driven Code Compliance Checking in Building Information Modeling](https://mlsanity.com/paper/2506.20551/) (2025)
- [CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking](https://mlsanity.com/paper/2403.02231/) (2024)
- [Using Large Language Models for the Interpretation of Building Regulations](https://mlsanity.com/paper/2407.21060/) (2024)
- [Multi-Agent LLM Orchestration Achieves Deterministic, High-Quality Decision Support for Incident Response](https://mlsanity.com/paper/2511.15755/) (2025)
- [Test-Driven AI Agent Definition (TDAD): Compiling Tool-Using Agents from Behavioral Specifications](https://mlsanity.com/paper/2603.08806/) (2026)
- [Scaling Test-Driven Code Generation from Functions to Classes: An Empirical Study](https://mlsanity.com/paper/2602.03557/) (2026)
- [Towards French Smart Building Code: Compliance Checking Based on Semantic Rules](https://mlsanity.com/paper/1910.00334/) (2019)
- [Automatic Building Code Review: A Case Study](https://mlsanity.com/paper/2510.02634/) (2025)
- [Automating Geometry-Intensive Compliance Checking in BIM: Graph-Based Semantic Reasoning Framework](https://mlsanity.com/paper/2606.12065/) (2026)

## Abstract (arXiv)

Verifying building compliance requires validating thousands of rules against large Building Information Modeling (BIM) designs, which is laborious, capital-intensive, and unscalable. Existing Automated Compliance Checkers (ACCs) are often difficult to generalize across different scenarios, as they are typically developed for highly specific rule sets and use cases. In addition, many ACCs are proprietary, meaning the underlying verification code is not released to end users, so users cannot verify whether their regulatory intent can be accurately captured. We introduce ARCHER (Agentic Rule and Compliance Harness for Executable Regulations), a test-driven, deterministically orchestrated multi-agent program-synthesis harness that generates auditable verification code from regulatory Codes of Practice, enabling transparent, adaptable, and scalable compliance checking. To characterize what makes agentic synthesis work, we evaluate a taxonomy of six harnesses of increasing agentic sophistication across four backbone models, spanning realistic data-governance tiers (from frontier third-party APIs to a fully on-premise open-weights model) on a novel dataset derived from real-world compliance scenarios. ARCHER's deterministic multi-agent orchestration achieves the highest accuracy for every backbone, improving mean union accuracy by 82% over a naive single-pass prompting baseline. Our cost-accuracy analysis further shows that using the ARCHER harness, a self-hosted open-weights model can reach 97.8% of frontier-API accuracy at a quarter of the cost, making data-sovereign compliance checking practical.