AI Dev 26 x SF | Or Dagan: Optimizing Accuracy, Cost, and Latency in Real-World Agents

2026-05-22 · Source: DeepLearningAI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, long

Summary

AI21 Labs' Or Dagan presented A21 Maestro, an automated approach to optimize real-world AI agents for accuracy, cost, and latency, addressing the challenges of manual configuration. The system tackles the "vicious cycle" where improving one metric often degrades others. Maestro operates in two phases: an offline build-time component efficiently samples the vast configuration space to train an "action model" that predicts performance metrics for various agent actions. This model then powers an online, budget-aware runtime that dynamically orchestrates agent execution, selecting optimal paths based on user-defined cost and latency constraints. The approach leverages techniques like prompt optimization, vertical scaling (e.g., critique-repair loops), and horizontal scaling (e.g., best-of-N sampling, model ensembles). A21 Maestro achieved leading performance on benchmarks like Browsecom Plus and Deep Research Bench, offering an efficient, observable, and future-proof solution to agent optimization.

Key takeaway

For AI Engineers building production agents, manually optimizing for accuracy, cost, and latency is unsustainable and inefficient. You should explore automated agent orchestration platforms like A21 Maestro to dynamically manage execution paths and configurations. This approach allows you to define budget and latency constraints, ensuring your agents operate optimally without constant manual tuning, and provides future-proof adaptability to new models or data drift.

Key insights

Automating agent optimization across accuracy, cost, and latency requires dynamic orchestration based on predictive models and efficient configuration sampling.

Principles

• Agent optimization involves a Pareto frontier of tradeoffs.
• Combining diverse models improves accuracy and efficiency.
• Dynamic execution strategies adapt to budget constraints.

Method

A21 Maestro trains an action model offline by efficiently sampling agent configurations, then uses this model at runtime to dynamically orchestrate agent execution based on predicted accuracy, cost, and latency.

In practice

• Use best-of-N sampling to boost agent accuracy.
• Employ model ensembles for better performance and cost.
• Implement critique-repair loops for vertical scaling.

Topics

• AI Agents
• Agent Optimization
• LLM Orchestration
• Cost-Latency-Accuracy Tradeoffs
• A21 Maestro
• Pareto Frontier

Best for: AI Architect, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by DeepLearningAI.