In the rapidly evolving landscape of AI-assisted development, many coders often find themselves tethered to a single “agentic” platform—be it GitHub Copilot, Claude Code, or another. This loyalty was understandable when each tool demanded its own unique context management. However, a silent revolution is underway with AGENTS.md, a universal context standard that’s fundamentally transforming how developers interact with AI coding assistants.

AGENTS.md offers a groundbreaking solution, allowing a single project specification to be leveraged across diverse platforms like GitHub Copilot, Claude Code, Gemini CLI, and OpenAI Codex. This means you can define your project once and then choose the best AI tool for the task at hand, fostering a flexible and powerful multi-platform workflow.

To demonstrate this paradigm shift, an experiment was conducted: building the same complex application—Conway’s Game of Life with real-time pattern recognition and a retro arcade aesthetic—using a consistent 2,000-word AGENTS.md specification across three different AI coding tools. The results shed light on the current capabilities of these agents and the immense potential of a unified context standard.

What is AGENTS.md?

At its core, AGENTS.md is a standardized Markdown file designed to provide comprehensive context to AI coding assistants. Envision it as a living project brief within your repository, detailing requirements, technical specifications, coding preferences, architectural decisions, and any other crucial information an AI needs to operate effectively.

Its key advantages are:
* Universal Compatibility: It functions seamlessly across leading AI coding tools, including GitHub Copilot, Claude Code, and Gemini CLI.
* Simplicity: It utilizes plain Markdown, eliminating the need for complex, proprietary syntax.
* Persistent Context: The AI reads this file every time, ensuring it always has the most up-to-date project understanding without repetitive explanations from the developer.

An AGENTS.md file typically contains a project overview, technical requirements, desired file structure, coding standards, dependencies, and setup instructions. It lives in the project’s root directory, with some platforms even supporting multi-level AGENTS.md files for more granular context. While GitHub Copilot also supports Instructions.md, AGENTS.md provides a universally recognized standard, with Claude Code and Gemini CLI specifically using it as their primary context source. This unified approach means you write your project’s context once, and multiple AI tools can instantly understand and act upon it.

The Multi-Platform Experiment

The experiment involved a challenging task: building Conway’s Game of Life with advanced features like real-time pattern recognition for gliders, oscillators, and still lifes, all wrapped in a distinct retro arcade visual style. The detailed AGENTS.md specification covered everything from cellular automaton logic to visual effects like CRT scanlines and glow.

The same specification was then given to three distinct platforms:
1. GitHub Copilot with GPT-5: A widely used daily driver for many developers.
2. Claude Code: Anthropic’s command-line coding agent.
3. Gemini CLI: Google’s terminal-based coding tool.

Each tool started from a clean slate, referencing the identical AGENTS.md file, with no human intervention or iterative fixes—a true one-shot build to assess their capabilities.

Diverse Approaches, Varied Outcomes

All three agents successfully produced working implementations, but their processes, final products, and overall developer experiences were notably different.

• Claude Code: The Meticulous Planner
  Claude Code distinguished itself by first pausing to plan. It meticulously read the specification, then proposed a detailed roadmap—including file structure, implementation strategy, and feature priorities—before seeking approval. This collaborative “AI proposes, human approves” approach resulted in the most polished one-shot implementation. Its pattern recognition was accurate, visual effects robust, and the code structure exemplary, feeling truly production-ready.

• Gemini CLI: The Transparent Craftsman
  Gemini CLI delivered an implementation that was visually striking and true to the retro aesthetic. What stood out was its honesty; it acknowledged its incomplete state, explicitly stating, “Next, I will focus on enhancing the pattern detection to recognize more complex patterns like gliders and other oscillators, as specified in the project requirements.” This transparency was highly valued, offering a functional product while clearly outlining areas needing further development.

• GitHub Copilot + GPT-5: The Capable Generalist
  Copilot quickly generated a solid, clean codebase with a working game and retro aesthetic. While impressive, its pattern recognition, particularly the color-coding of oscillators, wasn’t fully compliant with the specification. It was a strong, functional output, but less polished in certain core features compared to Claude Code.

Objective Analysis by AI

To move beyond subjective impressions, Grok Code Fast 1, another AI, conducted a blind code review of all three implementations against the original AGENTS.md specification.

• Claude Code: 9/10
  • Strengths: Excellent pattern recognition (gliders, still lifes, oscillators), advanced features (afterglow, extinction alerts, stable pattern detection), full retro arcade UI.
  • Weaknesses: Missing LWSS spaceship detection, potential performance lag in dense grids.
• GitHub Copilot + GPT-5: 9/10 (Subjectively closer to 8/10)
  • Strengths: Strong pattern recognition (gliders, LWSS, oscillators, still lifes), advanced visual features (scanlines, vignette, vector glow), balanced retro aesthetic.
  • Weaknesses: Oscillator detection relied on state comparison, potentially missing edge cases.
• Gemini CLI: 6/10
  • Strengths: Clean, functional UI with good retro styling.
  • Weaknesses: Severely limited pattern detection (only basic still life and blinker), no stability/extinction detection, basic trail effects.

The Transformative Workflow Insight

The most significant takeaway from this experiment isn’t just about comparing AI capabilities, but the revelation that using multiple AI coding tools on the same project is now not only viable but potentially optimal. Both Claude Code and Gemini CLI are easily installed via Homebrew, making experimentation effortless.

If you’re already using Copilot in VSCode, you can seamlessly open a terminal pane and consult Claude Code or Gemini CLI for alternative perspectives. Because both tools read the same AGENTS.md file, you’re not restarting; you’re simply getting a different agent’s approach to the identical problem, leveraging their unique strengths. This seamless multi-tool integration, facilitated by AGENTS.md, provides a powerful advantage, especially when one agent might struggle with a particular challenge.

The Future of AI-Assisted Development

We are at a pivotal juncture in AI-assisted development. These tools have moved beyond mere experimentation and are proving genuinely capable. Claude Code delivered near-production-ready code in a single pass. Copilot provided a robust and reliable implementation. Even Gemini, despite its pattern recognition shortcomings, produced a functional and visually appealing application, showcasing its potential for rapid iteration.

The AGENTS.md standard is the enabler, making multi-tool workflows practical by eliminating the need to re-contextualize for each assistant. This isn’t about abandoning your preferred AI; it’s about embracing the diverse strengths different tools offer. Claude Code excelled in planning and catching edge cases. Copilot showed stronger spaceship detection. Gemini brought a compelling aesthetic, even where its pattern detection lagged. The infrastructure for this multi-tool approach is already in place.

Experience It Yourself

The implementations from this experiment are publicly available:
* Claude Code’s version
* GitHub Copilot’s version
* Gemini CLI’s version

The foundational AGENTS.md file that powered all three can be found here.

If you’re currently using one AI coding assistant, consider dedicating fifteen minutes to experiment with another. The barrier to entry is low, and the insights gained from observing different AI approaches to the same problem are invaluable. Embrace the flexibility that AGENTS.md brings to your coding workflow.