Data to 14 Oct 2026

Agentic AI Software Engineers: Programming with Trust

arXiv 2502.13767 ↗First posted 19 Feb 2025cs.SE cs.AIcited by 20

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust equal to or even greater than the trust established by human-driven software engineering practices. The recent trend toward LLM agents offers a path toward integrating the power of LLMs to create new code with the power of analysis tools to increase trust in the code. This opinion piece comments on whether LLM agents could dominate software engineering workflows in the future and whether the focus of programming will shift from programming at scale to programming with trust.

Key facts. Cited by 20 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 8 of its 13 references that are in the map. First posted on arXiv 19 Feb 2025.

Builds on

8 of 13 references.

Closest ancestors · 3

Same direction, newest first

Other references · 5

2024AutoCodeRover: Autonomous Program Improvementcited by 278
2025SWE-Lancer: Can Frontier LLMs Earn 1 Million from Real-World Freelance Software Engineering?cited by 105
2024Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLMcited by 64
2023A Systematic Review on Fostering Appropriate Trust in Human-AI Interactioncited by 21
2024Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programmingcited by 20

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 10; 2026: 10.

20 papers.

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2025Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectoriescited by 81
2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AIcited by 42
2025The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineeringcited by 9
2026AgentStepper: Interactive Debugging of Software Development Agentscited by 9
2025PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contractscited by 8
2025Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overviewcited by 7
2026Agentic AI Empowered Intent-Based Networking for 6Gcited by 7
2025When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?cited by 3
2025LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recoverycited by 2
2026Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecyclescited by 2
2025How Far Are We from Generating Missing Modalities with Foundation Models?cited by 1
2025AutoCodeSherpa: Symbolic Explanations in AI Coding Agentscited by 1

8 more

2026RAG-Reflect: Agentic Retrieval-Augmented Generation with Reflections for Comment-Driven Code Maintenance on Stack Overflowcited by 1
2025Will AI replace Software Engineers? Do not hold your breathcited by 0
2026LLMs + Security = Troublecited by 0
2026Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Softwarecited by 0
2026Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agentscited by 0
2026TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimizationcited by 0
2026Towards Risk-free AI Agent Deploymentcited by 0
2026Do Tool Calls Execute as Intended? Measuring and Repairing Intent-Execution Correspondence in LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way