Collaborating with agentic AI-powered integrated development environments, a practice termed "vibe coding," is transforming software development by compressing weeks of engineering work into hours or days. While modern tools can generate code, design architectures, and write tests across multiple languages, the rapid evolution of the tool landscape means the specific platform chosen matters less than the developer's ability to collaborate effectively with these systems. The central challenge has shifted from writing code to managing the human-AI partnership to ensure productivity, cost control, and maintainability. A recent analysis of building an intelligent search system using Retrieval Augmented Generation (RAG) on a news dataset highlights critical risks in this approach. First, the principle of garbage-in-garbage-out remains valid; even with confident AI output, ambiguous prompts lead to solutions that drift from user needs. Second, prompting remains a core competency. Poor instructions can rapidly consume API limits without yielding a usable product. Third, over-engineering poses a significant threat. Because AI can effortlessly generate complex architectures, developers may inadvertently accept designs that introduce unnecessary components, making the system difficult to maintain. To mitigate these risks, developers should adopt a structured workflow. The process begins with clear requirements. Rather than accepting open-ended goals like "build a search system," teams should define representative test queries to constrain the scope. Next, teams should generate an architecture document before writing code. By asking the AI to propose a design from scratch and then critically evaluating it against cost and complexity constraints, developers can guide the system toward a balanced solution. Validation is the next crucial step. Developers must stress-test the proposed architecture with edge cases, such as summarizing thousands of articles or handling complex cross-document comparisons. In the news dataset example, an initial AI design suggested sophisticated additions like Knowledge Graphs and Map-Reduce workflows. However, when challenged, the AI acknowledged that these complexities were excessive for the specific use case, and simpler methods like SQL joins or prompt adjustments were sufficient. This iterative cycle of asking the AI to critique its own design, followed by human judgment, prevents unnecessary bloat. Finally, human oversight must remain the final arbiter at every stage. The recommended workflow is a continuous loop: the human provides prompts, the AI generates output, both review the result, and the human provides feedback for iteration. While AI offers visibility into requirements and code, only humans can assess broader context, including business priorities, latency constraints, and long-term maintainability. By treating AI as a powerful tool rather than a replacement, organizations can harness its speed while ensuring the resulting software is robust, explainable, and aligned with user needs.