AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities

• URL: http://arxiv.org/abs/2508.11126v1
• Date: Fri, 15 Aug 2025 00:14:31 GMT
• Title: AI Agentic Programming: A Survey of Techniques, Challenges, and Opportunities
• Authors: Huanting Wang, Jingzhi Gong, Huawei Zhang, Zheng Wang,
• Abstract summary: AI agentic programming is an emerging paradigm in which large language models (LLMs) autonomously plan, execute, and interact with external tools.<n>This survey provides a comprehensive and timely review of AI agentic programming.
• Score: 4.678921535956218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI agentic programming is an emerging paradigm in which large language models (LLMs) autonomously plan, execute, and interact with external tools like compilers, debuggers, and version control systems to iteratively perform complex software development tasks. Unlike conventional code generation tools, agentic systems are capable of decomposing high-level goals, coordinating multi-step processes, and adapting their behavior based on intermediate feedback. These capabilities are transforming the software development practice. As this emerging field evolves rapidly, there is a need to define its scope, consolidate its technical foundations, and identify open research challenges. This survey provides a comprehensive and timely review of AI agentic programming. We introduce a taxonomy of agent behaviors and system architectures, and examine core techniques including planning, memory and context management, tool integration, and execution monitoring. We also analyze existing benchmarks and evaluation methodologies used to assess coding agent performance. Our study identifies several key challenges, including limitations in handling long context, a lack of persistent memory across tasks, and concerns around safety, alignment with user intent, and collaboration with human developers. We discuss emerging opportunities to improve the reliability, adaptability, and transparency of agentic systems. By synthesizing recent advances and outlining future directions, this survey aims to provide a foundation for research and development in building the next generation of intelligent and trustworthy AI coding agents.

Related papers

• From Prompt-Response to Goal-Directed Systems: The Evolution of Agentic AI Software Architecture [0.0]
  Agentic AI denotes an architectural transition from stateless, prompt-driven generative models toward goal-directed systems.<n>This paper examines this transition by connecting intelligent agent theories, with contemporary LLM-centric approaches.<n>The study identifies a convergence toward standardized agent loops, registries, and auditable control mechanisms.
  arXiv  Detail & Related papers  (2026-02-11T03:34:48Z)
• LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities [0.03437656066916039]
  This concept paper systematically reviews the emerging paradigm of LLM-based multi-agent systems.<n>We delve into a wide range of topics such as language model selection, SE evaluation benchmarks, state-of-the-art agentic frameworks and communication protocols.
  arXiv  Detail & Related papers  (2026-01-14T19:28:30Z)
• Adaptation of Agentic AI [162.63072848575695]
  We unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations.<n>We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI.<n>We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities.
  arXiv  Detail & Related papers  (2025-12-18T08:38:51Z)
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
  This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
  arXiv  Detail & Related papers  (2025-10-10T06:56:50Z)
• Agentic AI Frameworks: Architectures, Protocols, and Design Challenges [0.0]
  Large Language Models have ushered in a transformative paradigm in artificial intelligence, where intelligent agents exhibit goal-directed autonomy, contextual reasoning, and dynamic multi-agent coordination.<n>This paper provides a systematic review and comparative analysis of leading Agentic AI frameworks, including CrewAI, LangGraph, AutoGen, Semantic Kernel, Agno, Google ADK, and MetaGPT.<n>We identify key limitations, emerging trends, and open challenges in the field.
  arXiv  Detail & Related papers  (2025-08-13T19:16:18Z)
• A Survey on Agent Workflow -- Status and Future [2.817843718857682]
  This survey provides a comprehensive review of agent workflow systems.<n>We classify existing systems along two key dimensions: functional capabilities and architectural features.<n>We highlight common patterns, potential technical challenges, and emerging trends.
  arXiv  Detail & Related papers  (2025-08-02T04:15:30Z)
• A Survey on Code Generation with LLM-based Agents [61.474191493322415]
  Code generation agents powered by large language models (LLMs) are revolutionizing the software development paradigm.<n>LLMs are characterized by three core features.<n>This paper presents a systematic survey of the field of LLM-based code generation agents.
  arXiv  Detail & Related papers  (2025-07-31T18:17:36Z)
• A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence [87.08051686357206]
  Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static.<n>As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck.<n>This survey provides the first systematic and comprehensive review of self-evolving agents.
  arXiv  Detail & Related papers  (2025-07-28T17:59:05Z)
• Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Survey of LLM Agent Communication with MCP: A Software Design Pattern Centric Review [0.9208007322096533]
  The study revisits well-established patterns, including Mediator, Observer, Publish-Subscribe, and Broker.<n>The article concludes by outlining open challenges, potential security risks, and promising directions for advancing robust, interoperable, and scalable multi-agent ecosystems.
  arXiv  Detail & Related papers  (2025-05-26T09:11:17Z)
• Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI [0.36868085124383626]
  Review presents a comprehensive analysis of two emerging paradigms in AI-assisted software development: vibe coding and agentic coding.<n> Vibe coding emphasizes intuitive, human-in-the-loop interaction through prompt-based, conversational interaction.<n>Agentic coding enables autonomous software development through goal-driven agents capable of planning, executing, testing, and iterating tasks with minimal human intervention.
  arXiv  Detail & Related papers  (2025-05-26T03:00:21Z)
• From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review [1.4929298667651645]
  We present a comparison of benchmarks developed between 2019 and 2025 that evaluate large language models and autonomous AI agents.<n>We propose a taxonomy of approximately 60 benchmarks that cover knowledge reasoning, mathematical problem-solving, code generation and software engineering, factual grounding and retrieval, domain-specific evaluations, multimodal and embodied tasks, task orchestration, and interactive assessments.<n>We present real-world applications of autonomous AI agents in materials science, biomedical research, academic ideation, software engineering, synthetic data generation, mathematical problem-solving, geographic information systems, multimedia, healthcare, and finance.
  arXiv  Detail & Related papers  (2025-04-28T11:08:22Z)
• A Survey on (M)LLM-Based GUI Agents [62.57899977018417]
  Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction.<n>Recent advances in large language models and multimodal learning have revolutionized GUI automation across desktop, mobile, and web platforms.<n>This survey identifies key technical challenges, including accurate element localization, effective knowledge retrieval, long-horizon planning, and safety-aware execution control.
  arXiv  Detail & Related papers  (2025-03-27T17:58:31Z)
• Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
  Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence.<n>This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy.<n>Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
  arXiv  Detail & Related papers  (2025-03-27T12:50:17Z)
• Human-AI Experience in Integrated Development Environments: A Systematic Literature Review [2.1749194587826026]
  The integration of Artificial Intelligence into Integrated Development Environments (IDEs) is reshaping how developers interact with their tools.<n>This shift marks the emergence of Human-AI Experience in Integrated Development Environment (in-IDE HAX)<n>Research on in-IDE HAX remains fragmented, which highlights the need for a unified overview of current practices, challenges, and opportunities.
  arXiv  Detail & Related papers  (2025-03-08T12:40:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.