A Guide to Spec-Driven Development: Plan, Implement, and Validate
The DeepLearning.AI short course on Spec-Driven Development with Coding Agents introduces a professional paradigm shift in how we build applications using agentic coding assistance.
Rather than "vibe coding"—where developers rely on quick, high-level prompts that often lead to technical debt—SDD brings engineering rigour back to the process.
Agent is the muscle, but the SPEC is the brain - In this workflow, the human takes on the role of a senior architect, providing the "blueprints" (specifications), while the AI agent acts as the "muscle" to implement those designs.
The 1 Hour 20 Minute video course has 15 short lessons -
- Introduction - 4 mins
- Why spec−driven development? - 4 mins
- Workflow overview - 3 mins
- Set up your environment - 5 mins
- Setup - 1 min
- Creating the constitution - 10 mins
- Feature specification - 3 mins
- Feature implementation - 1 min
- Feature validation - 4 mins
- Project replanning - 6 mins
- The second feature phase - 6 mins
- The MVP - 3 mins
- Legacy support - 4 mins
- Build your own workflow - 6 mins
- Agent replaceability - 3 mins
- Conclusion - 1 min
- Quiz - 10 mins
The Paradigm Shift: SDD decouples the Specification (the "what" and "why") from the Implementation (the "how"). Developers focus on providing the unique context an agent lacks rather than writing code by hand.
Downstream Amplification: One of the primary benefits is the ability to control massive code changes with minor spec adjustments. For instance, changing one sentence in a spec to switch a database can automatically update hundreds of lines of code.
Eliminating Context Decay: Unlike chat-based "vibe coding," where history is often lost or the agent's memory becomes "full," specs provide a permanent, high-quality context that anchors the agent across multiple sessions avoiding the drift that comes from long prompt dialogues.
High Intent Fidelity: By defining problems, constraints, and success criteria upfront, the agent is significantly more likely to produce code that accurately matches your long-term goals.
The Project Constitution: Every project begins with a Constitution, consisting of three pillars: the Mission (vision/scope), the Tech Stack (technologies and constraints), and the Roadmap (sequence of phases).
Feature Development Loops: Work is organized into repeatable loops on isolated branches. Each loop follows a structured Plan, Implement, and Verify process to ensure feature quality and reduce cognitive debt.
The process where an agent creates something and we check or verify it is called human in the loop.
Because agents are so fast at writing code, software developers have lately been talking about cognitive debt, the mental load of tracking what your code is doing and how it has evolved.
To avoid drift, the agent can update the related specs simultaneously, ensuring the docs and code stay in sync.
Human-in-the-Loop Validation: The human's role shifts to supervisor. You review the agent’s work against the spec, using tools like code reviews and automated tests to ensure the implementation hasn't drifted from the original intent.
It’s important to “replan” between features in the SDD workflow, as it’s a great opportunity to reflect, update the constitution and roadmap, and fine-tune your process — a normal step, not a sign of failure.
Support for Brownfield Projects: SDD is not just for new "greenfield" projects. For legacy codebases, agents can "reverse engineer" a project constitution by exploring existing files, commits, and documentation.
Workflow Automation via Skills: A "Skill" in the context of agentic coding is a reusable package of instructions and resources that gives an agent a repeatable capability — handy when you catch yourself retyping the same prompts. Developers can create "Agent Skills"—packages of instructions that give agents repeatable capabilities, such as automatically updating changelogs or running complex validation suites.
Agent Flexibility and Standards: SDD is designed to be agent-agnostic. By using standards like the Agent Client Protocol (ACP) and Model Context Protocol (MCP), developers can switch between different AI models (like Claude or ChatGPT) without losing their established workflow.
The agent landscape moves fast. Keeping specs and tools independent of any single agent lets you switch tools without rebuilding your workflow.
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