arXiv:2605.27073v1 Announce Type: new Abstract: Adaptive orchestration of heterogeneous agents requires making sequential delegation decisions under uncertain and evolving agent behaviour, e.g., coordinating specialised AI models with varying reliability, cost, and response quality. While prior work on agent orchestration focuses on performance or cost, uncertainty in agent reliability and output distributions is typically not modelled explicitly at the orchestration level. In this work, we study the problem of adaptive orchestration of heterogeneous agents under uncertainty, where a meta-cont
The proliferation of specialized AI models and the increasing complexity of AI-driven systems necessitate more sophisticated orchestration mechanisms that account for real-world uncertainties.
This research addresses a critical limitation in current AI agent systems by explicitly modeling uncertainty, leading to more robust, reliable, and adaptable multi-agent AI deployments.
The ability to orchestrate diverse AI agents effectively, even with varying reliability and output quality, makes large-scale autonomous systems more practical and deployable across various sectors.
- · AI platform providers
- · Developers of multi-agent systems
- · Industries relying on complex automation
- · Cloud computing providers
- · Companies with rigid, non-adaptive AI systems
- · Manual coordination roles in complex AI workflows
More resilient and efficient deployment of AI agent swarms across diverse applications.
Acceleration of autonomous system development and adoption in critical, real-time environments.
Enhanced overall reliability of AI-driven services, leading to greater public trust and broader societal integration of AI.
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Read at arXiv cs.LG