arxiv: 2604.16323 · v2 · submitted 2026-03-03 · 💻 cs.SE · cs.AI

Recognition: no theorem link

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

Matteo Casserini , Alessandro Facchini , Andrea Ferrario

Authors on Pith no claims yet

Pith reviewed 2026-05-15 16:58 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords agentic entropyexplainabilityautonomous agentssoftware developmentcognitive driftcausal graphsprocess monitoringintent telemetry

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The pith

Autonomous coding agents drift from architectural intent in ways that code diffs cannot detect.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that high-speed autonomous coding agents create agentic entropy, a gradual divergence from original architectural goals that traditional review methods miss. Code diffs and human-centered explainable AI focus only on local outputs instead of tracking decisions across time and tool uses. To address this, the authors introduce a process-oriented explainability framework built on three pillars: conformity seeding to establish baselines, reasoning monitoring to observe decision flows, and a causal graph interface to map influences across boundaries. This provides intent-level visibility that helps both casual vibe coders see hidden structures and professional developers ground their reviews better. Treating cognitive drift as a core issue alongside code quality aims to keep human oversight meaningful as agents take on more work.

Core claim

Agentic entropy is the accumulating divergence between agentic actions and architectural intent in autonomous coding systems. Traditional code diff-based reviews and HCXAI methods fail to capture this global behavior because they examine isolated outputs rather than processes unfolding over time, tool calls, and architectural lines. The proposed solution is a process-oriented explainability framework with three pillars—conformity seeding, reasoning monitoring, and a causal graph interface—that supplies intent-level telemetry to support substantive human oversight without replacing existing practices.

What carries the argument

The three-pillar process-oriented explainability framework consisting of conformity seeding to initialize alignment, reasoning monitoring to track decision paths, and a causal graph interface to visualize cross-boundary influences, which together generate telemetry on agent intent.

If this is right

Reviewers can access not only changed code but the sequence of reasoning steps that led to those changes.
Lay users engaged in vibe coding receive structural insights that functional success alone would hide.
Professional developers obtain richer context for code reviews at no added overhead.
Cognitive drift becomes a tracked concern parallel to traditional code quality metrics.
The framework supports the minimum comprehension level needed for ongoing agentic oversight to stay effective.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Integrating such monitoring into development environments could flag potential drifts in real time during agent sessions.
Over time, this might influence how organizations audit and certify AI-assisted software projects.
Similar process tracking could apply to other agentic domains like autonomous testing or deployment pipelines.
Empirical tests on large-scale projects would clarify whether the causal graphs remain usable without growing too complex.

Load-bearing premise

Traditional code diff and HCXAI methods inherently miss the global aspects of agent behavior, and the new framework can supply enough human understanding for oversight without creating extra drift or work.

What would settle it

Compare review outcomes in paired sessions where one group uses only diffs and the other uses the framework, checking whether the framework group identifies more cases of intent divergence.

Figures

Figures reproduced from arXiv: 2604.16323 by Alessandro Facchini, Andrea Ferrario, Matteo Casserini.

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper flags a real oversight issue with AI coding agents but leaves the proposed framework as an untested sketch.

read the letter

The paper names a gap in overseeing AI coding agents by introducing 'agentic entropy' as the accumulating divergence from architectural intent. It claims that code diffs and existing explainability methods only handle local outputs and miss the global picture across time and tool calls. The proposed fix is a process-oriented framework built on three pillars—conformity seeding, reasoning monitoring, and a causal graph interface—that aims to deliver intent-level telemetry for both lay users and professional developers. What stands out is the practical angle. The authors correctly note that functional success in generated code can hide structural drift, which matters more as agents handle larger tasks. Framing the framework as something that adds to review practices rather than replacing them keeps the proposal grounded. The main limitation is the absence of any operational detail. The pillars are described in broad terms but without definitions, algorithms, or even a simple example of how they would work on an actual agent trace. There are no metrics proposed for entropy or for the quality of the telemetry, and no discussion of implementation costs or risks of new forms of drift. This makes the central claim—that the framework supplies the minimum comprehension needed for substantive oversight—impossible to evaluate from the text. The work shows clear thinking about the problem but stops short of providing the mechanisms or evidence that would let a reader test the idea. It builds on explainability literature without claiming to reinvent it. This paper would interest people working on AI agents for software engineering, especially those focused on human oversight and governance. A reading group could use it to discuss the limits of current review methods. For peer review, it deserves consideration because the underlying concern is real and timely, though it would likely require substantial additions to move beyond a sketch. I recommend sending it to referees with feedback to develop the framework into something more concrete.

Referee Report

3 major / 2 minor

Summary. The paper introduces 'agentic entropy' as the accumulating divergence between autonomous coding agents' actions and architectural intent. It claims that traditional code diff-based reviews and HCXAI methods address only local outputs and fail to capture global agentic behavior across time, tool calls, and boundaries. To address this, the authors propose a process-oriented explainability framework built on three pillars—conformity seeding, reasoning monitoring, and a causal graph interface—that supplies intent-level telemetry for substantive human oversight. The framework is said to complement existing practices and is illustrated for two user profiles: lay 'vibe coders' gaining structural visibility and professional developers obtaining richer context without added overhead.

Significance. If the framework can be operationalized and validated, the work would address a timely gap in oversight for agentic software development by elevating process-level cognitive drift to a first-class concern alongside code quality. It correctly identifies that velocity in agentic workflows outpaces conventional review methods and offers a complementary telemetry approach. However, because the manuscript remains entirely conceptual with no formal definitions, algorithms, examples, or empirical results, its significance is currently prospective rather than demonstrated.

major comments (3)

[Framework Proposal] The section describing the three-pillar framework provides no operational definitions, pseudocode, or construction details for conformity seeding, reasoning monitoring, or the causal graph interface. Without these, the central claim that the pillars together deliver 'intent-level telemetry' sufficient for 'minimum human comprehension' cannot be evaluated or falsified.
[User Profiles and Demonstration] The demonstration across user profiles asserts that the framework supplies structural visibility for lay users and contextual grounding for professionals 'without increased overhead,' yet no worked example, trace, metric, or comparison against diff/HCXAI baselines is supplied to support this.
[Introduction and Motivation] The gap analysis asserts that diff-based and HCXAI methods 'inherently fail to capture global agentic behaviour,' but offers no concrete analysis of specific failure modes, cited limitations from the HCXAI literature, or quantitative illustration of the claimed shortfall.

minor comments (2)

[Terminology] The term 'agentic entropy' is introduced without a formal definition or relation to existing entropy concepts in information theory or software engineering, which could be clarified to aid adoption.
[Conclusion] The manuscript would benefit from an explicit future-work subsection outlining planned operationalization, metrics, and evaluation protocols to guide readers on how the proposal can be tested.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and insightful review. We agree that the conceptual nature of the manuscript requires additional operational detail to strengthen evaluability, and we commit to revisions that address the identified gaps while maintaining the position-paper focus on process-level oversight. Below we respond point-by-point to the major comments.

read point-by-point responses

Referee: [Framework Proposal] The section describing the three-pillar framework provides no operational definitions, pseudocode, or construction details for conformity seeding, reasoning monitoring, or the causal graph interface. Without these, the central claim that the pillars together deliver 'intent-level telemetry' sufficient for 'minimum human comprehension' cannot be evaluated or falsified.

Authors: We acknowledge that the current description remains at a conceptual level. In the revised manuscript we will add operational definitions and high-level pseudocode for each pillar: conformity seeding will be defined as the initialization of architectural intent anchors at workflow start via explicit constraint injection; reasoning monitoring as continuous logging of agent rationales with drift detection against seeded intents; and the causal graph interface as a directed acyclic graph with nodes as tool invocations and edges encoding causal dependencies derived from reasoning traces. These additions will make the 'intent-level telemetry' claim concrete and subject to evaluation. revision: yes
Referee: [User Profiles and Demonstration] The demonstration across user profiles asserts that the framework supplies structural visibility for lay users and contextual grounding for professionals 'without increased overhead,' yet no worked example, trace, metric, or comparison against diff/HCXAI baselines is supplied to support this.

Authors: The manuscript currently uses descriptive scenarios rather than empirical demonstrations. We will incorporate a concrete worked example of an agentic session (including a step-by-step trace of tool calls and drift detection) showing visibility gains for the 'vibe coder' profile and contextual support for professionals. A qualitative comparison table against diff-based reviews and HCXAI methods will be added, explicitly noting that quantitative overhead metrics lie outside the scope of this conceptual proposal. revision: partial
Referee: [Introduction and Motivation] The gap analysis asserts that diff-based and HCXAI methods 'inherently fail to capture global agentic behaviour,' but offers no concrete analysis of specific failure modes, cited limitations from the HCXAI literature, or quantitative illustration of the claimed shortfall.

Authors: We will expand the introduction with specific failure-mode examples, such as cumulative architectural drift across sequential refactoring tool calls that remains invisible in isolated diffs. Relevant HCXAI literature on limitations in sequential and multi-step explainability will be cited. Illustrative scenarios will be added to demonstrate the shortfall in capturing global behavior, while acknowledging that quantitative shortfall measurements are beyond the current conceptual scope. revision: yes

Circularity Check

0 steps flagged

Conceptual proposal with no equations, fits, or self-referential derivations

full rationale

The manuscript is a forward-looking proposal that defines 'agentic entropy' and introduces a three-pillar framework (conformity seeding, reasoning monitoring, causal graph interface) as a conceptual response to limitations of diff-based and HCXAI methods. No equations, parameters, or quantitative derivations appear in the provided text. The central claims rest on definitional assertions rather than any reduction of outputs to inputs by construction, fitted subsets, or load-bearing self-citations. The work is therefore self-contained as a design sketch and exhibits no circularity under the enumerated patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The paper rests on domain assumptions about agent behavior and introduces a new named phenomenon without independent empirical grounding.

axioms (2)

domain assumption Agentic actions accumulate divergence from architectural intent in ways not captured by local code diffs or existing HCXAI methods.
Foundational premise for defining agentic entropy and motivating the new framework.
ad hoc to paper A process-oriented explainability approach can supply intent-level telemetry sufficient for substantive human oversight.
Assumed effectiveness of the proposed three-pillar structure.

invented entities (1)

agentic entropy no independent evidence
purpose: To label the systemic drift between agentic actions and architectural intent.
New term introduced to frame the oversight problem.

pith-pipeline@v0.9.0 · 5482 in / 1461 out tokens · 44337 ms · 2026-05-15T16:58:08.744148+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · 1 internal anchor

[1]

Hao Li, Haoxiang Zhang, and Ahmed E. Hassan. The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering, 2025. arXiv:2507.15003 [cs.SE]

work page internal anchor Pith review arXiv 2025
[2]

How AI Is Transforming Work at Anthropic, December 2, 2025.URL: https://anthropic.com/research/ how-ai-is-transforming-work-at-anthropic

Saffron Huang, Bryan Seethor, Esin Durmus, Kunal Handa, Miles McCain, Michael Stern, and Deep Ganguli. How AI Is Transforming Work at Anthropic, December 2, 2025.URL: https://anthropic.com/research/ how-ai-is-transforming-work-at-anthropic. 5 Addressing Agentic Entropy in Agentic Software Development

work page 2025
[3]

2024 Accelerate State of DevOps

DORA Team at Google Cloud. 2024 Accelerate State of DevOps. Annual Research Report, Google Cloud, Sunnyvale, CA, USA, October 2024.URL: https://dora.dev/research/2024/dora- report/2024- dora-accelerate-state-of-devops-report.pdf

work page 2024
[4]

Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task, 2025

Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Iris Braunstein, and Pattie Maes. Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task, 2025. arXiv:2506.08872 [cs.AI]

work page arXiv 2025
[5]

Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding, 2026

Fangzhou Lin, Qianwen Ge, Lingyu Xu, Peiran Li, Xiangbo Gao, Shuo Xing, Kazunori Yamada, Ziming Zhang, Haichong Zhang, and Zhengzhong Tu. Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding, 2026. arXiv:2602.00854 [cs.AI]

work page arXiv 2026
[6]

Meir M. Lehman. Programs, Life Cycles, and Laws of Software Evolution.Proceedings of the IEEE, 68(9):1060– 1076, 1980.DOI:10.1109/PROC.1980.11805

work page doi:10.1109/proc.1980.11805 1980
[7]

Exploring the Effectiveness of LLM based Test-driven Interactive Code Generation: User Study and Empirical Evaluation

Sarah Fakhoury, Aaditya Naik, Georgios Sakkas, Saikat Chakraborty, Madan Musuvathi, and Shuvendu Lahiri. Exploring the Effectiveness of LLM based Test-driven Interactive Code Generation: User Study and Empirical Evaluation. InProceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, ICSE-Companion ’24, ...

work page doi:10.1145/3639478.3643525 2024
[8]

James, and Nadia Polikarpova

Shraddha Barke, Michael B. James, and Nadia Polikarpova. Grounded Copilot: How Programmers Interact with Code-Generating Models.Proc. ACM Program. Lang., 7(OOPSLA1), April 2023.DOI:10.1145/3586030

work page doi:10.1145/3586030 2023
[9]

Conveying Agent Behavior to People, 2021.URL:https://hcxai.jimdosite.com

Ofra Amir. Conveying Agent Behavior to People, 2021.URL:https://hcxai.jimdosite.com

work page 2021
[10]

Riedl, and Tim Miller

Ronal Singh, Upol Ehsan, Marc Cheong, Mark O. Riedl, and Tim Miller. LEx: A Framework for Operationalising Layers of AI Explanations, 2021.URL:https://hcxai.jimdosite.com

work page 2021
[11]

Chi, Quoc V

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V . Le, and Denny Zhou. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. InProceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, New Orleans, LA, USA. Curran Associates Inc., 2022.DOI:10.5555/3...

work page doi:10.5555/3600270.3602070 2022
[12]

DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning

Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Peiyi Wang, Qihao Zhu, Runxin Xu, Ruoyu Zhang, Shirong Ma, Xiao Bi, et al. DeepSeek-R1 Incentivizes Reasoning in LLMs Through Reinforcement Learning. Nature, 645(8081):633–638, 2025.DOI:10.1038/s41586-025-09422-z

work page doi:10.1038/s41586-025-09422-z 2025
[13]

TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems, 2025

Shaina Raza, Ranjan Sapkota, Manoj Karkee, and Christos Emmanouilidis. TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems, 2025. arXiv: 2506.04133 [cs.AI]

work page arXiv 2025
[14]

With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systems, 2025

Shaun Khoo, Jessica Foo, and Roy Ka-Wei Lee. With Great Capabilities Come Great Responsibilities: Introducing the Agentic Risk & Capability Framework for Governing Agentic AI Systems, 2025. arXiv: 2512 . 22211 [cs.AI]

work page 2025
[15]

Hassan, Hao Li, Dayi Lin, Bram Adams, Tse-Hsun Chen, Yutaro Kashiwa, and Dong Qiu

Ahmed E. Hassan, Hao Li, Dayi Lin, Bram Adams, Tse-Hsun Chen, Yutaro Kashiwa, and Dong Qiu. Agentic Software Engineering: Foundational Pillars and a Research Roadmap, 2025. arXiv:2509.06216 [cs.SE]

work page arXiv 2025
[16]

Udo-Imeh, Bonan Kou, and Tianyi Zhang

Samia Kabir, David N. Udo-Imeh, Bonan Kou, and Tianyi Zhang. Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems, CHI ’24, Honolulu, HI, USA. Association for Computing Machinery, 2024.DOI:10.1145/3613904.3642596

work page doi:10.1145/3613904.3642596 2024
[17]

AI Copilot Code Quality: 2025 Look Back at 12 Months of Data, January 2025.URL: https://www.gitclear.com/ai_assistant_code_quality_2025_research

GitClear Research. AI Copilot Code Quality: 2025 Look Back at 12 Months of Data, January 2025.URL: https://www.gitclear.com/ai_assistant_code_quality_2025_research

work page 2025
[18]

Evaluating Large Language Models in Class-Level Code Generation

Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou. Evaluating Large Language Models in Class-Level Code Generation. InProceedings of the IEEE/ACM 46th International Conference on Software Engineering, ICSE ’24, Lisbon, Portugal. Association for Computing Machinery, 2024.DOI:10.11...

work page doi:10.1145/3597503.3639219 2024