Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap

Abstract: AI coding assistants and autonomous agents are becoming integral to software development workflows, reshaping how code is produced, reviewed, and maintained. While recent research has focused mainly on the capabilities and impacts of productivity of these systems, much less attention has been paid to accountability: who is responsible when agents generate, modify, or recommend code? In practice, accountability is defined through the Terms of Service (ToS) and related policy documents that govern the use of AI-powered development tools. In this vision paper, we present a comparative analysis of the Terms of Service for widely used AI coding assistants and agent-enabled development tools. We examine how these documents allocate ownership, responsibility, liability, and disclosure obligations between tool providers and software developers, and we identify common patterns and divergences between providers. Our analysis reveals a consistent tendency to shift responsibility for correctness, safety, and legal compliance onto users, as well as substantial variation in how providers address issues such as indemnification, data reuse, and acceptable use. Based on these findings, we argue that existing policy frameworks are poorly aligned with increasingly agent-mediated and autonomous software development workflows. We outline a research roadmap for accountable agents in software engineering, identifying challenges and opportunities for modeling responsibility, designing governance artifacts, developing tooling that supports accountability, and conducting empirical studies of developers' perceptions and practices.

• The paper conducts a systematic review of Terms of Service to reveal misalignments between traditional legal frameworks and emerging agentic AI in coding.
• It demonstrates that current ToS models shift liability and risks to users by assuming necessary human oversight in agent-mediated workflows.
• The research roadmap outlines actionable directions for detailed responsibility modeling, computable governance, and robust audit tooling in software engineering.

Accountability Frameworks for Agentic AI in Software Engineering

Overview

"Accountable Agents in Software Engineering: An Analysis of Terms of Service and a Research Roadmap" (2605.04532) confronts the critical but understudied issue of socio-technical accountability within the rapidly evolving domain of AI coding assistants and autonomous agents in software engineering workflows. Rather than pursuing technical advances in agentic coding systems or empirical evaluations of developer productivity, the paper rigorously interrogates the contractual frameworks that define ownership, responsibility, liability, and usage constraints in practice. Specifically, it dissects the Terms of Service (ToS) and related governance artifacts from a diverse corpus of nine prominent AI-enabled development tools, surfacing patterns and discrepancies in how providers operationalize accountability and identifying points of policy misalignment with emerging agentic system capabilities. The paper concludes by articulating a detailed and actionable research roadmap to bridge the chasm between legacy human-centered contractual models and the operational realities of agent-mediated software engineering.

Comparative Analysis of Governance Artifacts

The paper employs a qualitative methodology, executing a systematic close reading of 14 ToS and governance documents across the AI development tool landscape (including OpenAI, Anthropic, GitHub Copilot, JetBrains, Google Gemini, AWS CodeWhisperer, Replit, Cursor/Anysphere, and Sourcegraph). The analysis is structured along four core dimensions:

• Ownership and IP: Consistently, providers assign output-related intellectual property rights to end users, allowing them the full spectrum of use, modification, and distribution.
• Responsibility and Liability: Across the corpus, strong disclaimers and indemnification clauses place onus for correctness, legal compliance, and the downstream impact of generated code on users.
• Data Governance: Significant variation exists, from explicit reuse for model improvement (especially for public content) to more granular boundaries between user (input/output) data and system-generated analytics.
• Acceptable Use and Delegation: While most governance assumes human oversight, language is typically broad enough to cover delegated, automated, or agentic tool usage.

The central empirical finding is the consistent coupling of output rights with a categorical shift of risk, legal exposure, and compliance obligations to the user. This structure, derived from the ToS of leading providers, is reinforced with clauses that limit provider liability (e.g., liability caps), require user indemnification, and in many cases, provide output guarantees that are either excluded or conditional.

Divergences in Provider Posture

While a baseline consensus exists regarding the allocation of output rights and user-centric responsibility, the paper identifies meaningful points of divergence among providers:

• Indemnification Scope: Some providers (Replit, Cursor) fully transfer litigation risk to users. Others (OpenAI, especially in enterprise contexts) condition indemnification on the correct usage of safeguards and output filters.
• Data Usage for Model Improvement: Practices range from unrestricted reuse of user prompts and public code (Replit, OpenAI), to more nuanced separation of content and system telemetry (JetBrains).
• Framing of AI Fallibility: Warranty disclaimers vary from generic (Google, GitHub) to highly explicit, all-caps warnings (Anthropic), with most tools requiring developers to validate and audit AI outputs independently.
• Anticipation of Agentic Use: Some newer documents explicitly accommodate delegated agentic behavior, including automated action, system manipulation, and restriction against misrepresentation of automated output as human-authored.

Misalignment with Agentic Automation

The contractual regime critically presupposes close human review and traditional validation, a model suitable for autocomplete-like assistants but increasingly obsolete for agentic systems that operate semi-autonomously, execute multi-step plans, or directly manipulate software artifacts. The central claim is that current ToS frameworks are misaligned with the operational reality of autonomous agents: they collapse agent delegation into human use, failing to represent nuanced distinctions in authority, supervision, and responsibility across planning, enactment, and verification phases. They provide limited guidance or audit structure when multi-agent systems or agent-initiated changes propagate through modern CI/CD pipelines without direct human sign-off.

Research Roadmap and Implications

The paper’s roadmap systematically identifies priorities and actionable directions for the near-future research agenda:

Responsibility Modeling for Autonomous Workflows

There is strong need for formal models that assign responsibility at a finer granularity than the current all-or-nothing user ownership paradigm. This includes delineating responsibility for different workflow stages (prompt construction, plan approval, autonomous execution, merge/deployment, post-hoc audit, etc.) and developing representations for operationalized, traceable supervision boundaries.

Governance-aware Agentic Systems

The absence of technical enforcement for governance constraints (beyond contract language) creates both liability gaps and lost opportunities for principled agent design. Embedding computable governance signals (e.g., AGENTS.md, CLAUDE.md), project-local policy artifacts, and conditional access mechanisms can enable agents to actively enforce policy compliance and increase the operational significance of governance signals—prefiguring automated policy-aware agents.

Tooling for Legible, Auditable Agent Action

Tooling to record fine-grained, human- and machine-readable provenance—including agent prompt logs, version identifiers, and output attribution—is necessary to bridge contractual responsibility and practical auditability. Automated meta-documentation linking agent actions to downstream code changes is required to ensure compliance can scale with agent activity.

Input Accountability and Interaction Provenance

Current ToS draw boundaries around inputs and outputs without representing the full session structure of agentic prompting, tool-invocation chains, and iterative reasoning. Future policy and technical work must account for the legal and ethical implications of input provenance, prompt reuse, and the diffusion of sensitive or biased content through iterative agentic interactions.

Empirical Studies on Practice, Interpretation, and Realignment

There is a clear theoretical and empirical gap in how developers internalize ToS language, adapt team processes, and negotiate responsibility in the event of agent failure or security breach. Empirical research must investigate the second-order impacts (e.g., increased risk aversion, misallocation of review burden, or false security) induced by the current contractual risk allocation.

Conclusion

This work demonstrates that industry-standard ToS for AI-powered software engineering tools systematically link output-related rights to user responsibility, operationalizing accountability as a legal formalism tailored to assistive, not autonomous, agent modalities. As development practice shifts toward agent-mediated workflows with less direct human oversight, the existing governance framework is rendered increasingly inadequate—potentially undermining both organizational risk management and developer autonomy. The research agenda articulated in the paper is substantively aligned with advancing operational, auditable, and technically actionable accountability in the age of agentic automation. Addressing the identified gaps will require sustained, theoretically rigorous work at the intersection of software engineering, AI governance, legal informatics, and developer experience design.