AppLooper: An Agentic Application Engineering Loop for Accountable Release with Virtual-User Feedback
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Much existing research on coding agents organizes application development as an iterative loop of requirement interpretation, implementation, tool execution, evaluation, and repair. As these loops run longer, requirements may drift; users may lose awareness of the current state and rationale for changes; and generated applications may remain insufficiently grounded in target users' contexts and needs. Application engineering therefore requires a mechanism connecting owner intent, target-user experience, development changes, and responsibility for release. We present AppLooper, a human--coding-agent--virtual-user application engineering loop for accountable release. An application owner confirms frozen requirements, supplies feedback, inspects candidates, and retains final release authority. A development agent produces and revises versioned candidates. A virtual-user agent cohort executes interface scenarios grounded in target users and contexts of use. Besides, an owner-intent simulation agent retests only requirements, constraints, and feedback explicitly confirmed by the owner, abstaining when evidence is insufficient. A testing agent performs read-only developmental checks by reproducing reported failures, running existing regression tests, and exercising the current candidate through its browser interface. The orchestration layer groups the resulting findings and routes them into development revision, targeted retesting, and owner inspection. AppLooper binds requirements, feedback sources, interface targets, development changes, retesting outcomes, owner interactions, and release decisions to specific versions. It thereby extends sustained coding-agent iteration into a traceable and reviewable lifecycle in which humans retain final responsibility for release. Source code is available at https://github.com/ZihongHe/applooper.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 17 of its 35 references that are in the map. First posted on arXiv 14 Aug 2026.
Builds on
17 of 35 references.
Closest ancestors · 6
Same direction, newest first
- 2026Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents3 Jun
- 2024Human-In-the-Loop Software Development Agents19 Nov
- 2024Large Language Model-Based Agents for Software Engineering: A Survey4 Sep
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agents23 Jul
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
- 2023ChatDev: Communicative Agents for Software Development16 Jul
Other references · 11
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
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- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
- 2023Teaching Large Language Models to Self-Debugcited by 963
- 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
- 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
- 2026Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluationscited by 36
- 2023Investigating and Designing for Trust in AI-powered Code Generation Toolscited by 34
- 2025Mixture-of-Personas Language Models for Population Simulationcited by 20
- 2024Ivie: Lightweight Anchored Explanations of Just-Generated Codecited by 14
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