Building 3 AI Agents on a $0 Budget: Lessons in Tool-Use, RAG, and Code Execution

A computer science graduate has demonstrated how to build functional AI agents using Google Gemini's free tier, proving that advanced agentic systems can be developed without financial investment. The project features three distinct agents: a research assistant utilizing web search tools, a RAG-based Q&A system for local document analysis, and a data analysis agent capable of executing Python code. By focusing on core patterns like tool selection, vector-based retrieval, and code interpretation, the author highlights that the primary challenge in AI development is not just the model calls, but the engineering 'plumbing'—such as managing rate limits and data chunking. The project emphasizes that developers can gain a deep, practical understanding of how modern AI systems function under the hood by building these architectures from scratch, with all source code now available on GitHub for those looking to replicate the results.