Abstract
This disclosure describes an adaptive computational budgeting framework for artificial intelligence (AI) agents that can help manage and control resource consumption. Unlike existing rudimentary controls, the described framework allows organizations and/or users to define multi-factor budgets for specific tasks, considering processing time, LLM tokens, API calls, etc. An adaptive budget engine is deployed to monitor real-time resource consumption and enforce budgets through actions such as graceful termination, quality throttling, or checkpointing, preventing runaway processes and unexpected high costs. The described framework offers hierarchical control via administrative consoles and user interfaces, provides cost attribution for billing, and includes observability and reporting features. This approach enables granular cost control, enhances predictability, optimizes resource allocation, and empowers users to balance task quality with resource constraints, ultimately leading to more efficient and predictable AI agent operations.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Start, Johannes and Lunney, John, "Adaptive Computational Budgeting for AI Agents in Collaborative Environments", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/8602