AgentAssay: Token-Efficient Regression Testing for Non-Deterministic AI Agent Workflows

Summary

You cannot diff an agent's output against a golden file, because the output legitimately differs every run. AgentAssay tests the distribution instead of the sample: stochastic verdicts over repeated runs, with a sampling strategy that reaches a decision in a fraction of the tokens a naive repeat-and-compare would burn.

Our summary of the paper. The full abstract is on arXiv. Preprint, not peer reviewed.

Authors

• Varun Pratap Bhardwaj

Cite this paper

@misc{bhardwaj2026agentassay,
  title        = {AgentAssay: Token-Efficient Regression Testing for Non-Deterministic AI Agent Workflows},
  author       = {Varun Pratap Bhardwaj},
  year         = {2026},
  eprint       = {2603.02601},
  archivePrefix= {arXiv},
  primaryClass = {cs.AI},
  url          = {https://arxiv.org/abs/2603.02601}
}