Introducing Showboat and Rodney, so agents can demo what they’ve built

· Source: Simon Willison's Weblog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

On February 10, 2026, two new CLI tools, Showboat and Rodney, were released to address the challenge of coding agents demonstrating their work. Showboat, a Go binary optionally wrapped in Python, helps agents construct Markdown documents that detail their code's functionality, automatically embedding command outputs and images. Rodney, built on the Go Rod library, provides CLI browser automation, enabling agents to control Chrome for web-based demonstrations, including opening URLs, executing JavaScript, clicking elements, and taking screenshots. These tools aim to reduce manual QA time by allowing agents to create verifiable demonstrations, minimizing the need for expensive QA agent swarms. The author developed both tools primarily using Claude Code for web via an iPhone app, emphasizing their utility in asynchronous coding agent environments.

Key takeaway

For Machine Learning Engineers integrating coding agents into development workflows, you should adopt tools like Showboat and Rodney to streamline agent output verification. These tools enable agents to autonomously generate clear, verifiable demonstrations of their code's functionality, significantly reducing the manual QA burden and ensuring delivered code actually works as intended. Incorporate these into your agent prompts to foster more reliable and transparent development cycles.

Key insights

Showboat and Rodney enable coding agents to autonomously demonstrate their work through verifiable documentation and browser automation.

Principles

Method

Agents use Showboat commands (init, note, exec, image) to build Markdown demos, with Rodney providing browser interaction (start, open, js, click, screenshot) for web-based features, all guided by comprehensive `--help` output.

In practice

Topics

Code references

Best for: Machine Learning Engineer, Software Engineer, AI Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Simon Willison's Weblog.