About The Workshop

With the rapid advances in AI, we have witnessed a wave of breakthroughs in autonomous AI agents. Today's agents can write and debug code, solve competition-level mathematical problems, conduct deep research, and are increasingly deployed in everyday products and workflows.

However, most of these agents are designed to maximize task performance. They are largely unaware of the resources they consume, including the compute, energy, memory, latency, tool cost, and data required to train them. An agent that issues dozens of frontier model API calls, spawns many sub-agents, or repeatedly searches and re-plans may top a benchmark, but does so at a cost that is prohibitive in real deployment. As capabilities grow, this hidden resource footprint grows with them.

Designing resource-aware agents that reason about, account for, and adapt to their own resource budgets is becoming increasingly important. Resource awareness is essential to ensure that everyone, not only those with access to large compute budgets, can benefit from agents, and to remove a key barrier to the widespread real-world deployment of AI agents.

We organize this workshop to bring together academic and industrial experts to explore how to design agents that are both efficient and effective, from training to system deployment.