Kill the Ceremony: Intent-Driven Development and the Minimum Viable Spec

2026-07-12 · Source: Towards AI - Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

The article introduces Intent-Driven Software Development (IDSD) as an alternative to Spec-Driven Development (SDD), aiming to find a middle ground between rigid SDD and chaotic "vibe-coding" for AI-powered software development. IDSD focuses on defining "what matters" (intent, expectations, constraints, failure conditions) and letting the AI system determine "how to implement it." The author demonstrates IDSD by building a local AI image exploration tool, specifying intent for continuous image variation, user control, and local model integration, with a default image size of 512x512 and a target generation interval of 10 seconds or less. This approach allows for flexibility when requirements change, as only the intent needs updating, not the entire architectural blueprint.

Key takeaway

For engineering leads and technical product managers struggling with rigid spec-driven development or chaotic vibe-coding, Intent-Driven Software Development (IDSD) offers a flexible middle ground. By clearly defining your project's intent, expectations, constraints, and failure conditions, you empower AI to handle implementation details, significantly reducing rework when requirements shift. Adopt IDSD to accelerate development and maintain quality without over-specifying.

Key insights

Intent-Driven Software Development (IDSD) focuses on human-defined intent, expectations, context, constraints, and failure conditions, allowing AI to determine implementation details.

Principles

• Minimize process to maximize results.
• Humans define "what matters," AI implements "how."
• Turn every failure into an explicit rule.

Method

IDSD involves defining intent, expectations, context, constraints, and failure conditions. The AI system then generates the implementation details, allowing for flexible adaptation to changes by updating only the intent.

In practice

• Define intent, expectations, context, constraints, failure conditions.
• Provide mock-ups for UI/UX vision.
• Update intent files, not just code, for fixes.

Topics

• Intent-Driven Development
• Spec-Driven Development
• AI Software Engineering
• Minimum Viable Spec
• Software Development Process
• Prompt Engineering

Code references

• codysandahl/local-ai-image-explorer
• arvindrk/extract-design-system

Best for: Director of AI/ML, AI Product Manager, AI Engineer

Related on AIssential

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• AI-Infused Development Needs More Than Prompts — Effective AI-infused development hinges on explicit intent and robust control, not just code generation speed.
• From Vibe Coding to Spec-Driven Development — Spec-driven development with AI agents offers a structured approach to building software, enhancing collaboration and maintaining context.
• What’s your “I can’t believe AI can do this” moment? — AI, when well-directed, acts as a powerful assistant, automating complex tasks and expanding individual capabilities.
• Spec-driven development: The AI engineering workflow at Notion | Ryan Nystrom — AI agents streamline engineering workflows, automating tasks from meeting prep to code generation and spec-driven development.
• AI-driven Software Development: A Pragmatic Path to Agentic Development Processes — Systematic AI integration across the SDLC requires a "harness" for controlled, agentic development.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.