Study Examines Sources of Variability in AI Agent Outputs Across Multiple Runs
A new arXiv paper analyzes why AI agent systems produce different outputs for identical inputs, identifying token sampling as one explicit source of variability. The research distinguishes between intrinsic sources like token generation and extrinsic factors such as environmental changes and infrastructure effects. Understanding these layers of variability is important for predicting and controlling AI agent behavior in deployed systems.
Researchers have published a detailed analysis of variability in agentic AI systems, which can produce different plans, tool calls, code edits, or answers when given the same request. The paper identifies token generation—where language models sample from probability distributions over possible next tokens—as an explicit intrinsic source of this variability, showing how small differences in token selection can cascade into different downstream behaviors. Beyond token sampling, the authors identify extrinsic sources of variability including changing environments, live data, serving infrastructure, batch effects, and numerical precision details. By separating these layers, the manuscript clarifies what it means to describe agentic AI systems as stochastic, when variability can be reproduced under controlled conditions, and why deterministic code execution may still produce different real-world behavior. This distinction has practical implications for understanding and controlling AI agent reliability in production settings.
What's missing
The paper's own limitations and open questions are not detailed in the abstract provided. Additionally, specific empirical results, quantitative measurements of variability magnitude, or case studies demonstrating the cascading effects of token sampling differences are not included in the abstract.
What different sources said
- arXiv cs.AICenter
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