---
id: "lib-2026-10-23-can-ai-agents-help-robotic-engineers-lessons-from"
type: "library"
title: "Can AI Agents Help Robotic Engineers? Lessons from Two Focus Days in Industry"
lang: "EN"
org: "arXiv"
source_type: "Paper"
date: "2026-10-23"
week: "2026-W44"
url: "https://nosov.ai/library/lib-2026-10-23-can-ai-agents-help-robotic-engineers-lessons-from/"
original_url: "https://arxiv.org/abs/2610.32636"
tier: "B"
tier_name: "Useful, limited"
tier_reason: "University of Passau case study with a crossover design, real industrial tasks and assessment by the firm's own team lead; careful and transparent, but four engineers and 42 task solutions cannot support general conclusions."
summary_basis: "full-text"
original_minutes: "40"
tags: ["enablement", "coding agents", "manufacturing", "learning curve"]
---

# Can AI Agents Help Robotic Engineers? Lessons from Two Focus Days in Industry

Tier B (Useful, limited): University of Passau case study with a crossover design, real industrial tasks and assessment by the firm's own team lead; careful and transparent, but four engineers and 42 task solutions cannot support general conclusions.

Summary based on the full text.

## In short

A preprint (arXiv, 23 October 2026) by Salim Fares and Steffen Herbold, University of Passau, on a robot-programming firm, AKE Technologies, trying a coding agent on its daily work. Four experienced engineers solved six typical tasks with and without Claude Code running an on-premise open-weight model (GPT-OSS 120B). The agent made them slower and their code worse on most tasks; it helped only on highly repetitive trajectory code. Results improved after eight weeks and a reconfigured agent, and prior AI experience mattered more than robotics experience.

## Key numbers

- 3 of 12 vs 9 of 12: tasks finished within the time limit with the agent versus without AI on the first focus day
- 1 of 9 vs 5 of 9: tasks finished in time with the agent versus without AI on the second focus day
- 25 vs 11: programming-standard violations with versus without the agent on day one (16 vs 15 on day two)
- 5 of 8 vs 1 of 7: violation-free solutions with versus without the agent on day two, after the agent was reconfigured
- 8 weeks: gap between the two focus days, during which engineers could use the agent in daily work

## What it reports

- Day one: 3 of 12 tasks finished with the agent versus 9 of 12 without; agent work took at least as long on every task. Team-lead ratings averaged functionality 1.7 versus 2.3, code quality 1.8 versus 2.5, readability 2.1 versus 2.8.
- Engineers used the agent for every change, even adding a comma, so it regenerated whole modules; long conversations drifted. Prepared skills were almost never invoked.
- Between days the researchers replaced the skills with automatic project context: the ABB RAPID manual, task descriptions and a CLAUDE.md file with coding rules.
- Day two: 1 of 9 finished with the agent versus 5 of 9 without, but standards adherence reversed (5 of 8 agent solutions violation-free versus 1 of 7 without). Functionality still lagged.
- Trajectory generation was the one clear win: the most AI-experienced engineer finished in 15 minutes, as fast as the most senior engineer working without AI.
- The engineer who rarely used AI finished no task with the agent.

## How they got it

AB/BA crossover over two full focus days: each engineer did each task type once with and once without the agent, with different task variants on day two. Time-boxed tasks (30-60 minutes) were defined and graded by the firm's head of robot programming; a validator checked standards; questionnaires and chat logs were collected. Inference ran on one local H100 because customer data cannot go to the cloud.

## Limits

- Four engineers (three on day two); no statistical generalisation, as the authors state.
- One model, chosen informally among five open-weight candidates.
- The terminal interface was unfamiliar to engineers used to graphical tools, which conflates tool design with agent capability.
- The agent had no access to the simulator's visual feedback.

## Why it matters for companies

For engineering teams outside mainstream software, an agent dropped in without training and domain context costs time. The fixes this study points to are cheap: load reference manuals and coding rules into the project context automatically, start with repetitive, well-specified tasks, and teach people when to delegate and when to edit by hand. It also shows that a confidential, on-premise setup is technically workable for a small firm, and that one-day productivity tests miss the learning curve.

Original: https://arxiv.org/abs/2610.32636