The Token Not Taken: Sampling, State, and the Stochasticity of AI Agents
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. At the core of many current agents is a foundation model, a large pretrained model adaptable to many downstream tasks, embedded in an orchestration loop that plans, calls tools, observes results, and updates state. One explicit intrinsic source of variability in such systems is token generation: the model computes scores over possible next tokens, the scores are converted into probabilities, and a decoder may sample tokens using a pseudo-random n...
| Authors | Muhammad Zia Hydari, Raja Iqbal |
|---|---|
| Primary category | cs.AI |
| Published | 8 June 2026 |
| Last updated at source | 2 July 2026 |
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