Imagine searching a vast codebase, not with keywords, but with the intent behind your search. That's the promise of semantic code search. But what happens when your intent is ambiguous, or your vocabulary doesn't match the code's terminology? Researchers are tackling this challenge with a novel approach: AI agents that enhance your search queries. These agents act like technical research assistants, using retrieval augmented generation (RAG) to gather information from the internet, specifically related to the GitHub repository you're searching. They then enrich your initial query with contextual details and technical nuances, ensuring that the search engine understands precisely what you're looking for. This is a game-changer for code discovery, eliminating the frustration of keyword mismatches and vague search results. Instead of sifting through irrelevant code snippets, developers can quickly pinpoint the exact functions and classes they need. This innovative approach has been implemented in a platform called RepoRift, demonstrating significant improvements in search accuracy. RepoRift leverages OpenAI's state-of-the-art text embeddings and a multi-stream comparison architecture to ensure comprehensive code retrieval. Early tests show that RepoRift dramatically outperforms existing methods, finding the correct code snippet within the top 10 results almost 80% of the time. While promising, challenges remain, particularly when dealing with code heavily reliant on external functions or libraries. Future research will likely focus on incorporating abstract syntax trees (ASTs) or similar code analysis techniques to address this limitation. The potential of AI agents in code search is undeniable, offering a glimpse into a future where developers can communicate their coding needs in natural language, and the right code magically appears.