Agent-First Development: A New Paradigm

In the rapidly evolving landscape of software development, we’re witnessing the emergence of a new paradigm that I’m tentatively calling “agent-first development.” This isn’t just another buzzword or a mere variation of existing practices – it represents a fundamental shift in how we approach software development through AI collaboration.

Beyond Vibe-Coding

Let’s be clear: this isn’t “vibe-coding” where we casually throw prompts at an AI and hope for the best. This is a methodical, structured approach to development that I’m calling “agent-first” development (though I’m still workshopping the name). The key distinction lies in how we architect our development process around AI collaboration from the ground up.

The Power of Small Contexts

One of the most crucial insights we’ve gained is that AI collaboration becomes significantly more effective when operating in smaller, well-defined contexts. Why? Because these contexts are easier to describe and manipulate, whether you’re communicating with humans or AI agents.

Think about it: when you’re trying to explain how to modify a specific component or function, it’s much easier to do so when that component has clear boundaries and a single responsibility. This principle has always been true for human collaboration (remember SOLID principles?), but it becomes even more critical when working with AI agents.

The Setup Journey

What’s particularly interesting about our current setup is how we got here. The entire infrastructure was built through AI collaboration, with only one non-agentic operation: setting up the FIREBASE_TOKEN secret, as described in our March 29, 2023 post. Everything else – from the initial repository structure to the deployment pipeline – was created through AI-human collaboration.

Transparency and Openness

A core principle of this approach is maintaining complete transparency in AI-generated content. This isn’t just about attribution; it’s about creating a clear trail of decision-making and enabling effective collaboration between human and AI agents. When every step is documented and every decision is traceable, it becomes much easier to:

Understand the reasoning behind specific implementations
Debug issues when they arise
Make improvements to the system
Share knowledge with other developers

Looking Forward

As we continue to refine this approach, we’re discovering that “agent-first” development isn’t just about using AI tools – it’s about rethinking how we structure our development processes to make them more conducive to human-AI collaboration. The emphasis on small contexts, clear documentation, and transparent processes isn’t new, but their importance is amplified in this new paradigm.

What’s particularly exciting is how this approach naturally leads to more maintainable, understandable code. By optimizing our development process for effective AI collaboration, we’re also creating better practices for human developers.

Stay tuned as we continue to explore and refine this approach. The term “agent-first” development might evolve, but the principles behind it – small contexts, transparency, and structured collaboration – are here to stay.

This article was originally created in commit 15fc73d4bca6034f3a7aee00d398f00b94cde145.