From individual vibe-coding to collaborative workflows with bots, the landscape is evolving rapidly. But how do these tools integrate into real-world teams with complex codebases and established processes?
Vibe-coding with AI
A top-down approach to programming with artificial intelligence, with real-life use cases and ready-to-use prompts.
ITDO Blog - Web, App, and Marketing Development Agency in Barcelona
After analyzing real-life experiences and country email list speaking with teams that are seriously adopting AI, the answer is clear: what works for a lone hacker doesn't scale with an engineering team. But there are effective ways to do it.
Vibe-coding as a starting point
In a previous article, I discussed the AI vibe-coding approach: a top-down methodology where prompts focus first on requirements, then architecture, and only then on code. This technique works very well on an individual level and is ideal for new ( greenfield ) projects .
The problem arises when you try to apply this in real-world environments, with large codebases, distributed teams, CI/CD, QA, and cross-platform reviews. This is where many current tools fall short: they're designed for single-player mode .
Real teams: productivity, context, and collaboration
Many teams have a particular relationship with AI tools. The gains aren't spectacular, and the reason lies in two key factors:
Large codebases : difficult to analyze without broad context.
Teamwork : Workflows break down if each dev interacts with their own bot.
Tools like Augment Code attempt to address this with RAG (Retrieval-Augmented Generation) architectures tailored to code environments, where the model accesses relevant context (classes, docs, technical decisions) to better respond.
Robots vs Iron Men: AI as an agent or as an assistant
Another key point is the philosophy of using AI: do we want it as a substitute (robots) or as an enhancer (Iron Man)?
Robots/autonomists : tools that create PRs without human supervision.
Assistants/Hybrids : AI integrates into your IDE, suggests changes, and you review and decide.
Today, the second option has clear advantages:
Shorter feedback cycles.
Better integration with the team.
Lower risk of critical errors.
The goal is to create super-teams, not replace people.