# Agentic Pipelines in Embedded Software Engineering: Emerging Practices and Challenges

arXiv: https://arxiv.org/abs/2601.10220 · HTML: https://mlsanity.com/paper/2601.10220/

## Key facts

- Cited by 2 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 9 of its 27 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Placed in

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

## Builds on (most cited first, 9 of 9)

- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [An Empirical Study of the Non-determinism of ChatGPT in Code Generation](https://mlsanity.com/paper/2308.02828/) (2023)
- [Promptify: Text-to-Image Generation through Interactive Prompt Exploration with Large Language Models](https://mlsanity.com/paper/2304.09337/) (2023)
- [The Future of AI-Driven Software Engineering](https://mlsanity.com/paper/2406.07737/) (2024)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [Prompts Are Programs Too! Understanding How Developers Build Software Containing Prompts](https://mlsanity.com/paper/2409.12447/) (2024)
- [Building Your Own Product Copilot: Challenges, Opportunities, and Needs](https://mlsanity.com/paper/2312.14231/) (2023)
- [Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products](https://mlsanity.com/paper/2410.12071/) (2024)
- [Dependability in Embedded Systems: A Survey of Fault Tolerance Methods and Software-Based Mitigation Techniques](https://mlsanity.com/paper/2404.10509/) (2024)

## Built on by (most cited first, 2 of 2)

- [Agentic AI in Industry: Adoption Level and Deployment Barriers](https://mlsanity.com/paper/2605.14675/) (2026)
- [Developing a Roadmap to an AI-first Organization: A Case Study in Embedded Software Development](https://mlsanity.com/paper/2609.30863/) (2026)

## Abstract (arXiv)

A new transformation is underway in software engineering, driven by the rapid adoption of generative AI in development workflows. Similar to how version control systems once automated manual coordination, AI tools are now beginning to automate many aspects of programming. For embedded software engineering organizations, however, this marks their first experience integrating AI into safety-critical and resource-constrained environments. The strict demands for determinism, reliability, and traceability pose unique challenges for adopting generative technologies. In this paper, we present findings from a qualitative study with ten senior experts from four companies who are evaluating generative AI-augmented development for embedded software. Through semi-structured focus group interviews and structured brainstorming sessions, we identified eleven emerging practices and fourteen challenges related to the orchestration, responsible governance, and sustainable adoption of generative AI tools. Our results show how embedded software engineering teams are rethinking workflows, roles, and toolchains to enable a sustainable transition toward agentic pipelines and generative AI-augmented development.