mlsanityData to 14 Oct 2026

Towards autonomous normative multi-agent systems for Human-AI software engineering teams

arXiv 2512.02329 ↗First posted 2 Dec 2025cs.SEcited by 2

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

This paper envisions a transformative paradigm in software engineering, where Artificial Intelligence, embodied in fully autonomous agents, becomes the primary driver of the core software development activities. We introduce a new class of software engineering agents, empowered by Large Language Models and equipped with beliefs, desires, intentions, and memory to enable human-like reasoning. These agents collaborate with humans and other agents to design, implement, test, and deploy software systems with a level of speed, reliability, and adaptability far beyond the current software development processes. Their coordination and collaboration are governed by norms expressed as deontic modalities - commitments, obligations, prohibitions and permissions - that regulate interactions and ensure regulatory compliance. These innovations establish a scalable, transparent and trustworthy framework for future Human-AI software engineering teams.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 34 references that are in the map. First posted on arXiv 2 Dec 2025.

Builds on

8 of 34 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025An LLM-based multi-agent framework for agile effort estimation17 Sep

Other references · 7

  1. 2024A Survey on Large Language Models for Code Generationcited by 658
  2. 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 131
  3. 2024Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-stepcited by 127
  4. 2023Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstractioncited by 46
  5. 2023An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applicationscited by 20
  6. 2025Procedural Memory Is Not All You Need: Bridging Cognitive Gaps in LLM-Based Agentscited by 9
  7. 2023NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledgecited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Human-AI Collaboration and the Transformation of Software Engineering Work
  2. 2026Rethinking Software Engineering for Agentic AI Systems
  3. 2025From Correctness to Collaboration: Toward a Human-Centered Framework for Evaluating AI Agent Behavior in Software Engineering
  4. 2026From Human Interfaces to Agent Interfaces: Rethinking Software Design in the Age of AI-Native Systems
  5. 2026Agyn: A Multi-Agent System for Team-Based Autonomous Software Engineering
  6. 2026Agentic Software: How AI Agents Are Restructuring the Software Paradigm
  7. 2026From Determinism to Delegation: AI-Native Software Engineering and the Evolution of the Agentic Engineer
  8. 2025The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering