Reaching SOTA Without Breaking the Bank: Using AI21 Maestro to optimize deep research agents

AI21 Maestro is an agent optimization framework designed to navigate the complex tradeoff between accuracy, cost, and latency in deploying AI models at scale. This blog outlines how Maestro addresses these challenges using two deep research benchmarks: BrowseComp-Plus, for retrieval precision and synthesis, and Deep Research Bench 1, for long-form report generation. Maestro automates the exploration of a vast configuration space, offering a complete Pareto frontier that visualizes achievable tradeoffs between quality, cost, and latency. Techniques such as model and agent setup, scaling strategies, and critique-repair loops are integrated to optimize performance. The framework systematically predicts and adjusts configurations in real-time, adapting to changing constraints without manual re-experimentation. By doing so, Maestro transforms agent optimization into a structured engineering practice, allowing teams to efficiently determine the best operating point according to their specific requirements.