GADR: Gathering Architecture Decision Records from Meeting Transcriptions

2026-08-18

AI Summary

本文提出GADR，一种多代理自校正工作流，从会议转录中提取架构决策并生成Nygard格式的ADR草稿，解决了非结构化输入导致的问题。

Abstract

Existing LLM-based approaches to Architecture Decision Record (ADR) generation share a critical and largely unexamined assumption: that input is already reasonably structured. In practice, architectural decisions emerge from informal, noisy meetings where choices are implicit, fragmented, and entangled with off-topic dialogue, precisely the conditions under which single-pass prompting degrades. This paper presents GADR, a multi-agent, self-correcting workflow that extracts architectural decisions from raw meeting transcriptions and generates Nygard-formatted ADR drafts. A feasibility study comprising five real project meeting transcripts, expert review by four senior architects, and evaluation by fifteen students provides initial evidence that the agentic workflow captures most expert-identified decisions and produces drafts participants found clear and useful, outperforming zero-shot and few-shot baselines in stability and structural adherence. The study also addresses the underexplored trade-off of RAG-based enrichment improving ADR depth while simultaneously risking transcript-unfaithful content, raising open questions about traceability in automated architectural documentation that we believe is worth the community's attention.

Problem

• Architecture Decision Record
• Meeting Transcriptions
• Informal Meetings
• Decision Extraction
• RAG-based Enrichment

Innovation

• multi-agent workflow
• self-correcting
• raw meeting transcriptions
• architecture decision records (ADR)
• retrieval-augmented generation (RAG)