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Coding agent workflow tested across Claude and Codex models

By PulseAugur Editorial · [1 sources] ·

A developer conducted an experiment to test the effectiveness of a Scrum workflow for coding agents across seven different AI models, including Anthropic's Claude (Haiku, Sonnet, Opus 5.5) and models from Codex CLI (GPT-6 Luna, GPT-6.1 Sol, GPT-5.6 Sol, GPT-6 Astra). The workflow improved process completion and reduced costs for some models, but did not consistently improve code correctness or benefit all models equally. A bug in the developer's own grading script was also identified and corrected, altering some of the initial findings. AI

IMPACT Provides insights into the practical application and limitations of current coding agent workflows across different LLMs.

RANK_REASON Developer's personal experiment and analysis of AI coding agents.

Read on dev.to — Claude Code tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Coding agent workflow tested across Claude and Codex models

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Developer's personal experiment and analysis of AI coding agents.
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COVERAGE [1]

  1. dev.to — Claude Code tag TIER_1 English(EN) · Huzair Saputra ·

    I ran 42 more agent sprints on a harder task. Here's what the workflow didn't fix.

    <p>In the <a href="https://dev.to/huzair_saputra/i-benchmarked-a-scrum-workflow-for-ai-coding-agents-heres-what-it-fixed-and-what-it-costs-4aja">first post</a> a Scrum workflow for coding agents looked clean: with the skill, 6 of 6 runs met every item of the Definition of Done, a…

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