# Agentic AI Software Engineers: Programming with Trust

arXiv: https://arxiv.org/abs/2502.13767 · HTML: https://mlsanity.com/paper/2502.13767/

## 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.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 8 of 8)

- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [AutoCodeRover: Autonomous Program Improvement](https://mlsanity.com/paper/2404.05427/) (2024)
- [RepairAgent: An Autonomous, LLM-Based Agent for Program Repair](https://mlsanity.com/paper/2403.17134/) (2024)
- [SWE-Lancer: Can Frontier LLMs Earn 1 Million from Real-World Freelance Software Engineering?](https://mlsanity.com/paper/2502.12115/) (2025)
- [SpecRover: Code Intent Extraction via LLMs](https://mlsanity.com/paper/2408.02232/) (2024)
- [Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM](https://mlsanity.com/paper/2402.00097/) (2024)
- [A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction](https://mlsanity.com/paper/2311.06305/) (2023)
- [Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming](https://mlsanity.com/paper/2405.01787/) (2024)

## Built on by (most cited first, 20 of 20)

- [Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories](https://mlsanity.com/paper/2506.18824/) (2025)
- [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025)
- [The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering](https://mlsanity.com/paper/2507.15003/) (2025)
- [AgentStepper: Interactive Debugging of Software Development Agents](https://mlsanity.com/paper/2602.06593/) (2026)
- [PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contracts](https://mlsanity.com/paper/2511.02780/) (2025)
- [Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview](https://mlsanity.com/paper/2503.10784/) (2025)
- [Agentic AI Empowered Intent-Based Networking for 6G](https://mlsanity.com/paper/2601.06640/) (2026)
- [When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?](https://mlsanity.com/paper/2510.17862/) (2025)
- [LinkAnchor: An Autonomous LLM-Based Agent for Issue-to-Commit Link Recovery](https://mlsanity.com/paper/2508.12232/) (2025)
- [Collaborator or Assistant? How AI Coding Agents Partition Work Across Pull Request Lifecycles](https://mlsanity.com/paper/2605.08017/) (2026)
- [How Far Are We from Generating Missing Modalities with Foundation Models?](https://mlsanity.com/paper/2506.03530/) (2025)
- [AutoCodeSherpa: Symbolic Explanations in AI Coding Agents](https://mlsanity.com/paper/2507.22414/) (2025)
- [RAG-Reflect: Agentic Retrieval-Augmented Generation with Reflections for Comment-Driven Code Maintenance on Stack Overflow](https://mlsanity.com/paper/2604.22217/) (2026)
- [Will AI replace Software Engineers? Do not hold your breath](https://mlsanity.com/paper/2502.20429/) (2025)
- [LLMs + Security = Trouble](https://mlsanity.com/paper/2602.08422/) (2026)
- [Govern the Repository, Not the Agent: Measuring Ecosystem-Level Risk in AI-Native Software](https://mlsanity.com/paper/2606.28235/) (2026)
- [Trust but Verify? Uncovering the Security Debt of Autonomous Coding Agents](https://mlsanity.com/paper/2607.12428/) (2026)
- [TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization](https://mlsanity.com/paper/2607.18161/) (2026)
- [Towards Risk-free AI Agent Deployment](https://mlsanity.com/paper/2608.16411/) (2026)
- [Do Tool Calls Execute as Intended? Measuring and Repairing Intent-Execution Correspondence in LLM Agents](https://mlsanity.com/paper/2610.04375/) (2026)

## 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.