arXiv:2606.08998v1 Announce Type: new Abstract: Agentic AI systems can behave differently across runs: the same request may produce a different plan, a different tool call, a different code edit, or a different final answer. Such variability arises from several layers that are often conflated. A foundation model is a large pretrained model, usually adaptable to many downstream tasks, that maps an input context to predictions over outputs. In many current agents, that model is embedded in an orchestration loop that plans, calls tools, observes results, and updates state. One explicit intrinsic
The paper addresses a critical challenge in the development and deployment of AI agents — their inherent variability — as agentic systems move from research to application.
Understanding the sources of variability in AI agent outputs is crucial for building reliable, trustworthy, and scalable autonomous systems in critical applications.
This research provides a framework for analyzing agent variability, allowing developers to design more robust AI agents and users to better anticipate their behavior and limitations.
- · AI developers
- · Enterprises adopting AI agents
- · AI safety researchers
- · Companies with unreliable AI agent products
- · Users unaware of AI agent variability
Improved reliability and predictability of AI agent performance across diverse tasks and environments.
Accelerated adoption of AI agents in more sensitive and high-stakes domains due to increased trust and control.
New regulatory frameworks specifically addressing AI agent variability and ensuring accountable deployment.
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Read at arXiv cs.AI