Large language models (LLMs) are impressive, but they still struggle with real-world problem-solving and complex logic. Enter Infant Agent, a new AI agent designed to tackle these challenges head-on. By integrating task-aware functions, a hierarchical management system, and a clever memory retrieval mechanism, Infant Agent empowers LLMs to think strategically and solve multi-step problems, all while dramatically cutting API costs. Imagine an LLM that can not only reason through complex math problems but also interact with tools and even edit code. Infant Agent makes this a reality. Tests on challenging datasets like SWE-bench-lite (for real-world coding problems) and AIME (a prestigious math competition) show significant performance leaps, boosting accuracy by up to 27 percentage points. The secret sauce? Infant Agent breaks down complex tasks into smaller, manageable chunks, assigning them to specialized 'hand-level' agents overseen by a 'brain-level' agent. This division of labor allows the system to use less powerful, more cost-effective LLMs for routine tasks, reserving the heavy-lifting for its 'brain.' Another key innovation is Infant Agent’s memory retrieval system. By intelligently storing and retrieving information, the agent avoids redundant computations, slashing API token costs by nearly 80%. This means more efficient and affordable AI solutions. While still under development, Infant Agent represents a promising step towards more practical and cost-effective AI. Future improvements include expanding to multimodal tasks (think image and sound processing) and even training a dedicated file-editing model. As AI continues to evolve, smart architectures like Infant Agent pave the way for more capable and accessible AI tools that can truly tackle real-world challenges.