Where Accountability Lives: Mapping Human Responsibility to Workflow Artifacts in Agentic Software Development
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Coding agents author commits, open pull requests and push code in production repositories. Responsibility is settled in two layers that do not refer to each other: platform controls gating what an agent may do, and provider terms allocating responsibility for its output. Objective: Where the two layers disagree at a workflow event, and what each states there about authority, execution, verification, consequence and record. Method: A qualitative document study of 121 items archived byte-exact: documentation for four agentic coding tools, the platform controls on their output, and eighteen policy documents from seven providers, read by deductive content analysis against an a-priori system of five dimensions and nine workflow events. An independent second coder blind-recoded the verification-mechanism classification, a complete enumeration and not a sample (Cohen's kappa 0.81, n = 13, on whether a mechanism compels; 0.75, n = 12, on who performs it). Results: Cursor's terms make the user responsible for evaluating the use of any suggestion; its documentation describes a product that performs that evaluation and records it as an approval. No collected artifact records that the verification the terms make the user's duty took place. At merge two tools compel a person, one documents an agent approving below a configured risk threshold, and one only advises. Six of the fifteen mechanisms that compel a check have a default the vendor states; nine have one this study inferred. Readers who had not made them refuted five of the eight absence claims; three survived, one materially qualified. Conclusions: The terms attach duty and consequence to output as a class, the platform records events, nothing records the duty discharged. One vendor documents a product that forms the approval judgement, and the terms allocate consequence against the artifact regardless.
Key facts. Cited by 1 paper 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 16 of its 51 references that are in the map. First posted on arXiv 16 Aug 2026.
Builds on
16 of 51 references.
Closest ancestors · 3
Same direction, newest first
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review17 May
- 2026On the Footprints of Reviewer Bots Feedback on Agentic Pull Requests in OSS GitHub Repositories27 Apr
- 2026AIDev: Studying AI Coding Agents on GitHub9 Feb
Other references · 13
- 2026Auditable Agentscited by 15
- 2022Automating Dependency Updates in Practice: An Exploratory Study on GitHub Dependabotcited by 13
- 2020An Empirical Study of Bots in Software Development -- Characteristics and Challenges from a Practitioner's Perspectivecited by 8
- 2021Quality Gatekeepers: Investigating the Effects ofCode Review Bots on Pull Request Activitiescited by 8
- 2026The Responsibility Vacuum: Organizational Failure in Scaled Agent Systemscited by 3
- 2026Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmapcited by 3
- 2026Proof of Execution: Runtime Verification for Governed AI Agent Actionscited by 3
- 2026Governed AI-Assisted Engineering: Graduated Human Oversight for Agentic Code Generation in Regulated Domainscited by 2
- 2025Quo Vadis, Code Review? Exploring the Future of Code Reviewcited by 1
- 2026Towards an Appropriate Level of Reliance on AI: A Preliminary Reliance-Control Framework for AI in Software Engineeringcited by 1
- 2026From Trust to Appropriate Reliance: Measurement Constructs in Human-AI Decision-Makingcited by 1
- 2026Redrawing the AI Map: A Theory of Accountability Boundaries in Agentic Ecosystemscited by 1
1 more
Built on by
Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2026Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents
- 2026Beyond the 'Diff': Addressing Agentic Entropy in Agentic Software Development
- 2026Correct Is Not Governed: Provenance Integrity in Agentic Workflows
- 2026Responsible Agentic AI Requires Explicit Provenance
- 2026From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation
- 2026The Work Behind Delegation: A Framework for Supervising AI Coding Agents
- 2026A Phased Workflow for Operating LLM-Based Coding Agents
- 2026Rethinking Software Engineering for Agentic AI Systems