To effectively scale an AI agent, teams should start small with a minimal viable product (MVP) and iteratively add more use cases as they master each one. This approach allows for cost-effective scaling and reduces the risk of over-investing in complex solutions. By organizing their team around code-focused senior product specialists who analyze performance data and refine processes, teams like Trilogy have successfully automated 60% of customer support in under two months. When choosing LLM models, consider using RAG to provide context, selecting versions that work best for specific tasks, testing different models for various use cases, and balancing prompt engineering costs against model upgrades. GPU hardware can be essential for large-scale AI projects, but smaller GPUs or serverless approaches can suffice for many use cases. A budget should prioritize time, effort, and strategic thinking over risk aversion, with allocations for keeping up with the AI landscape, evaluation-driven development, experimentation, and known problems. By starting small, scaling up carefully, and prioritizing continuous learning and experimentation, teams can achieve impressive results with limited budgets.