mlsanityData to 7 Oct 2026

Beyond the 'Diff': Addressing Agentic Entropy in Agentic Software Development

arXiv 2604.16323 ↗First posted 3 Mar 2026cs.SE cs.AIcited by 1

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

Abstract (arXiv)

As autonomous coding agents become deeply embedded in software development workflows, their high operational velocity introduces a critical oversight challenge: the accumulating divergence between agentic actions and architectural intent. We term this process agentic entropy: a systemic drift that traditional code diff-based and HCXAI methods fail to capture, as they address local outputs rather than global agentic behaviour. To close this gap, we propose a process-oriented explainability framework that exposes how agentic decisions unfold across time, tool calls, and architectural boundaries. Built around three pillars (conformity seeding, reasoning monitoring, and a causal graph interface) our approach provides intent-level telemetry that complements, rather than replaces, existing review practices. We demonstrate its relevance across two user profiles: lay users engaged in vibe coding, who gain structural visibility otherwise masked by functional success; and professional developers, who gain richer contextual grounding for code review without increased overhead. By treating cognitive drift as a first-class concern alongside code quality, our framework supports the minimum level of human comprehension required for agentic oversight to remain substantive.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 9 of its 19 references that are in the map. First posted on arXiv 3 Mar 2026.

Builds on

9 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Agentic Software Engineering: Foundational Pillars and a Research Roadmap7 Sep

Other references · 8

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  4. 2025Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Taskcited by 103
  5. 2025TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systemscited by 58
  6. 2023Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questionscited by 42
  7. 2026Position: Human-Centric AI Requires a Minimum Viable Level of Human Understandingcited by 6
  8. 2025With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systemscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Projecting the Emerging Mindset of SWE Agent by Launching a Wild Code Understanding Journey
  2. 2026Vibe-GUIDE: A Graph-based User Interface in IDEs for Oversight in Vibe Coding
  3. 2025Process-Centric Analysis of Agentic Software Systems
  4. 2025Agent READMEs: An Empirical Study of Context Files for Agentic Coding
  5. 2026Beyond Human-Readable: Rethinking Software Engineering Conventions for the Agentic Development Era
  6. 2026How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions
  7. 2026What Does an Agentic Software Engineering Benchmark Measure? Profiling Task Demands and Agent Behaviour Beyond What Category Labels Reveal
  8. 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests