AgentStepper: Interactive Debugging of Software Development Agents
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Software development agents powered by large language models (LLMs) have shown great promise in automating tasks like environment setup, issue solving, and program repair. Unfortunately, understanding and debugging such agents remain challenging due to their complex and dynamic nature. Developers must reason about trajectories of LLM queries, tool calls, and code modifications, but current techniques reveal little of this intermediate process in a comprehensible format. The key insight of this paper is that debugging software development agents shares many similarities with conventional debugging of software programs, yet requires a higher level of abstraction that raises the level from low-level implementation details to high-level agent actions. Drawing on this insight, we introduce AgentStepper, the first interactive debugger for LLM-based software engineering agents. AgentStepper enables developers to inspect, control, and interactively manipulate agent trajectories. AgentStepper represents trajectories as structured conversations among an LLM, the agent program, and tools. It supports breakpoints, stepwise execution, and live editing of prompts and tool invocations, while capturing and displaying intermediate repository-level code changes. Our evaluation applies AgentStepper to three state-of-the-art software development agents, ExecutionAgent, SWE-Agent, and RepairAgent, showing that integrating the approach into existing agents requires minor code changes (39-42 edited lines). Moreover, we report on a user study with twelve participants, indicating that AgentStepper improves the ability of participants to interpret trajectories (64% vs. 67% mean performance) and identify bugs in the agent's implementation (17% vs. 60% success rate), while reducing perceived workload (e.g., frustration reduced from 5.4/7.0 to 2.4/7.0) compared to conventional tools.
Key facts. Cited by 9 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 32 of its 63 references that are in the map. First posted on arXiv 6 Feb 2026.
Builds on
32 of 63 references.
Closest ancestors · 21
Same direction, newest first
- 2025A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Tasks2 Nov
- 2025Who is Introducing the Failure? Automatically Attributing Failures of Multi-Agent Systems via Spectrum Analysis17 Sep
- 2025Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling31 Jul
- 2025Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories23 Jun
- 2025Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study10 Jun
- 2025TRAIL: Trace Reasoning and Agentic Issue Localization13 May
- 2025SWE-smith: Scaling Data for Software Engineering Agents30 Apr
- 2025debug-gym: A Text-Based Environment for Interactive Debugging27 Mar
- 2025Agentic AI Software Engineers: Programming with Trust19 Feb
- 2025Agentic Bug Reproduction for Effective Automated Program Repair at Google3 Feb
- 2025Evaluating Agent-based Program Repair at Google13 Jan
- 2024Training Software Engineering Agents and Verifiers with SWE-Gym30 Dec
9 more
- 2024You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects13 Dec
- 2024Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projects9 Dec
- 2024Debug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks18 Oct
- 2024MarsCode Agent: AI-native Automated Bug Fixing2 Sep
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
- 2024MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution26 Mar
- 2024RepairAgent: An Autonomous, LLM-Based Agent for Program Repair25 Mar
- 2024AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systems14 Feb
Other references · 11
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2023CodePlan: Repository-level Coding using LLMs and Planningcited by 140
- 2024Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-stepcited by 127
- 2022Productivity Assessment of Neural Code Completioncited by 98
- 2024Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLMcited by 64
- 2025AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of Tool Orchestrationcited by 25
- 2025Issue2Test: Generating Reproducing Test Cases from Issue Reportscited by 18
- 2023GrACE: Generation using Associated Code Editscited by 16
- 2024Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agentscited by 8
- 2025RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelinescited by 3
Built on by
Citing papers per year: 2026: 9.
9 papers.
- 2026From Question Answering to Task Completion: A Survey on Agent System and Harness Designcited by 12
- 2026When Should an AI Workflow Release? Always-Valid Inference for Black-Box Generate-Verify Systemscited by 3
- 2026View-oriented Conversation Compiler for Agent Trace Analysiscited by 2
- 2026TraceView: Interactive Visualization of Agentic Program Repair Trajectoriescited by 2
- 2026InconLens: Interactive Visual Diagnosis of Behavioral Inconsistencies in LLM-based Agentic Systemscited by 1
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environmentscited by 1
- 2026NormCode Canvas: Making LLM Agentic Workflows Development Sustainable via Case-Based Reasoningcited by 0
- 2026Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Coursecited by 0
- 2026The Work Behind Delegation: A Framework for Supervising AI Coding Agentscited by 0
- 2026The Work Behind Delegation: A Framework for Supervising AI Coding Agents21 Sep 2026
- 2026Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course8 Sep 2026
- 2026Terminal Agents: A Survey of AI Agents in Command-Line Environments20 Aug 2026
- 2026TraceView: Interactive Visualization of Agentic Program Repair Trajectories20 Jun 2026
- 2026From Question Answering to Task Completion: A Survey on Agent System and Harness Design14 Jun 2026
- 2026When Should an AI Workflow Release? Always-Valid Inference for Black-Box Generate-Verify Systems13 May 2026
- 2026View-oriented Conversation Compiler for Agent Trace Analysis31 Mar 2026
- 2026InconLens: Interactive Visual Diagnosis of Behavioral Inconsistencies in LLM-based Agentic Systems30 Mar 2026
- 2026NormCode Canvas: Making LLM Agentic Workflows Development Sustainable via Case-Based Reasoning13 Mar 2026
Similar, not cited
Close in wording, no citation either way
- 2026Debug2Fix: Can Interactive Debugging Help Coding Agents Fix More Bugs?
- 2025Interactive Debugging and Steering of Multi-Agent AI Systems
- 2025Towards Adaptive Software Agents for Debugging
- 2026Empowering Autonomous Debugging Agents with Efficient Dynamic Analysis
- 2024UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging
- 2024Agentless: Demystifying LLM-based Software Engineering Agents
- 2026Evaluating LLM Agents on Automated Software Analysis Tasks
- 2026SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agents