# DevNous: An LLM-Based Multi-Agent System for Grounding IT Project Management in Unstructured Conversation

arXiv: https://arxiv.org/abs/2508.08761 · HTML: https://mlsanity.com/paper/2508.08761/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 80 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 24 of 24)

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- [Exploring Human-AI Collaboration in Agile: Customised LLM Meeting Assistants](https://mlsanity.com/paper/2404.14871/) (2024)
- [The Effectiveness of Large Language Models in Transforming Unstructured Text to Standardized Formats](https://mlsanity.com/paper/2503.02650/) (2025)

## Abstract (arXiv)

The manual translation of unstructured team dialogue into the structured artifacts required for Information Technology (IT) project governance is a critical bottleneck in modern information systems management. We introduce DevNous, a Large Language Model-based (LLM) multi-agent expert system, to automate this unstructured-to-structured translation process. DevNous integrates directly into team chat environments, identifying actionable intents from informal dialogue and managing stateful, multi-turn workflows for core administrative tasks like automated task formalization and progress summary synthesis. To quantitatively evaluate the system, we introduce a new benchmark of 160 realistic, interactive conversational turns. The dataset was manually annotated with a multi-label ground truth and is publicly available. On this benchmark, DevNous achieves an exact match turn accuracy of 81.3% and a multiset F1-Score of 0.845, providing strong evidence for its viability. The primary contributions of this work are twofold: (1) a validated architectural pattern for developing ambient administrative agents, and (2) the introduction of the first robust empirical baseline and public benchmark dataset for this challenging problem domain.