Greenfield
A greenfield project starts from a clean slate. There is no legacy codebase to respect, few users depend on the system, and the initial decisions are open.
This provides freedom, but also risk: early decisions can shape the entire product.
Difference from brownfield
| Context | Main characteristic |
|---|---|
| Greenfield | Built from scratch or nearly from scratch. |
| Brownfield | Work is done on an existing system. |
In greenfield, the main problem is not usually breaking what already exists. It is making too many implicit decisions without validating them.
Advantages
- Fewer legacy constraints.
- Greater architectural freedom.
- Room to experiment.
- Fewer initial dependencies.
- Easier to apply good practices from the start.
- Good fit with prototypes and vibe coding.
Risks
- Premature over-architecture.
- Lack of quality criteria.
- Technical debt from the very first prototype.
- Unnecessary rewrites.
- Too many decisions made by AI without review.
- Mistaking early velocity for real progress.
- Choosing technologies because they are trendy.
Greenfield and AI
AI can greatly accelerate a greenfield project: scaffolding, prototypes, first screens, APIs, tests, documentation, or initial structure.
But that speed can hide critical decisions:
- architecture;
- data model;
- security;
- error handling;
- dependencies;
- testing patterns;
- observability;
- coding standards.
Even without a legacy codebase, you still need a spec, a Definition of Done, and clear boundaries if the result is going to production.
Best practices
- Use vibe coding to explore, not to lock in architecture without review.
- Define minimum quality criteria from the start.
- Write specs when the idea moves to production.
- Choose technology based on context, not on the model's automatic suggestions.
- Avoid building too much before validating value.
- Maintain tests and documentation from the early stages.
- Review AI-generated decisions.
Common mistake
Thinking that starting from scratch removes the need for discipline. Greenfield reduces inherited constraints, but it does not eliminate risks. Early decisions on architecture, data, and security can create technical debt very quickly, especially if AI generates the foundation without review.
Resources
🏦 SDD - Spec Driven Development en equipos ágiles [SDD - Spec-Driven Development in Agile Teams] (in Spanish)Skill Arena · Scrum Manager
🏦 Scrum en equipos con IA [Scrum in Teams with AI] (in Spanish)Skill Arena · Scrum Manager
📊 Guía didáctica SDD [SDD Teaching Guide] (in Spanish)Resources · Scrum Manager
📊 Guía didáctica Scrum en equipos con IA [Scrum in Teams with AI Teaching Guide] (in Spanish)Resources · Scrum Manager
See also
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