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

2025-08-14

AI Summary

AI agent programming—an emerging paradigm for autonomous software development—lacks a unified conceptual foundation, systematic taxonomy, and rigorous evaluation framework, hindering progress toward reliable, adaptive, and interpretable agents. Method: We conduct a systematic literature review to formally define the scope of AI agent programming, integrating foundational techniques in LLM-driven autonomous planning, execution, and tool orchestration. We propose the first unified behavioral taxonomy and architectural framework, decomposing agent capabilities into planning, memory, tool integration, and context management. Contribution/Results: Our analysis identifies critical open challenges—including long-context modeling, cross-task persistent memory, safety assurance, and human–agent collaboration—and critically assesses existing benchmarks and evaluation methodologies. The framework provides both theoretical grounding and practical guidance for developing next-generation AI programming agents, thereby advancing intelligent software engineering.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Defining scope and foundations of AI agentic programming
• Addressing challenges in memory, safety, and human collaboration
• Improving reliability and adaptability of autonomous coding agents

Innovation

• Autonomous planning and execution with LLMs
• Multi-step coordination and adaptive behavior
• Tool integration and execution monitoring