Mind the GeekConference45min
Spec-Driven Development with AI Agents: From High-Level Requirements to Working Software
This session introduces spec-driven development for AI coding agents, emphasizing structured workflows from clear requirements to trackable tasks. Attendees will learn how to control, review, and guide AI-generated code step-by-step to avoid unpredictability and “black box” results, ensuring reliable and transparent collaboration with AI tools.
Anton ArhipovJetBrains
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Friday, February 6, 13:10-13:55
Room B3
AI coding agents are powerful, but they often feel unpredictable. Without structure, they can jump into implementation, miss requirements, or generate code you can’t easily track. Spec-driven development is a practical approach that brings order to this process.
The method is simple: start with clear, high-level requirements, refine them into a detailed development plan, then break that plan into a task list with trackable steps. The AI agent works from these artifacts—requirements.md, plan.md, and tasks.md—instead of ad-hoc prompts. Each step becomes explicit, reviewable, and repeatable.
In this talk, I’ll show how to apply spec-driven development and explain my intuition for this approach. We’ll walk through an example: documenting requirements, generating a plan, creating tasks, and guiding the AI through execution one step at a time. Along the way, you’ll see techniques for controlling workflow, reviewing changes, and avoiding “black box” code generation.
If you’ve tried coding with AI tools but found them chaotic, this session will give you a framework to make them reliable partners.
The method is simple: start with clear, high-level requirements, refine them into a detailed development plan, then break that plan into a task list with trackable steps. The AI agent works from these artifacts—requirements.md, plan.md, and tasks.md—instead of ad-hoc prompts. Each step becomes explicit, reviewable, and repeatable.
In this talk, I’ll show how to apply spec-driven development and explain my intuition for this approach. We’ll walk through an example: documenting requirements, generating a plan, creating tasks, and guiding the AI through execution one step at a time. Along the way, you’ll see techniques for controlling workflow, reviewing changes, and avoiding “black box” code generation.
If you’ve tried coding with AI tools but found them chaotic, this session will give you a framework to make them reliable partners.
Anton Arhipov
Anton is a Developer Advocate at JetBrains, working with Kotlin, IntelliJ IDEA, and AI-driven developer tools. With a background in server-side development, he has spent over a decade building software for developers. A Java Champion since 2014, Anton speaks at conferences, shares insights on the Kotlin YouTube channel, and enjoys exploring new ideas in programming languages, AI-powered tooling, and developer workflows. He’s always experimenting with new tech, looking for ways to make coding more efficient and enjoyable.
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