Public evaluation

Visual-to-Code Pipeline

Evaluates if the vision agent correctly identifies a misaligned bearing and if Devin generates a valid frequency-reduction script.

Evaluation type

task based

Challenge

Autonomous Predictive Vision & Repair with Claude Agents and Cognition Devin

Difficulty

Advanced

Rigor

Unspecified

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Evaluation overview

How the linked challenge is judged: tasks, benchmarks, and criteria count.

Tasks

Benchmarks

Criteria

Task templates

Inputs and expected outputs.

Task 1

Visual-to-Code Pipeline

The agent must interpret a thermal image of a motor and trigger Devin to write a corrective script.

Input format

PNG (Thermal Image) + Current Machine Specs

Output format

Python Script + Validation Report

Linked challenge

Autonomous Predictive Vision & Repair with Claude Agents and Cognition Devin

Industrial vision systems are critical for identifying wear and tear in harsh factory conditions (dust, heat, vibration). This challenge tasks you with creating an agentic workflow using the Claude Agents SDK to process visual sensor data and Cognition Devin to autonomously generate corrective code for the PLC (Programmable Logic Controller). You will build an agent that uses Claude's vision capabilities to detect 'thermal bloom' or 'bearing misalignment' from simulated industrial camera frames. Once a defect is detected, the Claude agent must use the 'Computer Use' capability to navigate a simulated IDE where Cognition Devin is active. Devin will then be tasked with writing a Python-based predictive maintenance script that adjusts the machine's operating frequency to mitigate further damage until a human technician arrives. This addresses the transition of industrial automation into a 'software discipline' and the move toward 'agentic AI' in modern manufacturing.

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