Token-Efficient Stochastic Testing for AI Agents
Same statistical confidence. 83% less cost. Behavioral fingerprinting, adaptive budget optimization, and trace-first offline analysis.
Every prompt change, model swap, or tool update requires confidence that the agent still works. Fixed-N trial approaches burn tokens on stable scenarios while under-testing volatile ones.
Compact representations of agent behavior — tool sequences, state transitions, decision patterns. Low-dimensional signals for efficient change detection.
Calibrate trial counts per scenario based on measured variance. High-variance scenarios get more trials; stable ones get fewer. Zero waste.
Coverage metrics, contract checks, and mutation analysis on existing traces — zero additional token cost for comprehensive reliability assessment.
Varun Pratap Bhardwaj, 2026
Introduces behavioral fingerprinting, adaptive budget optimization, and trace-first offline analysis for testing AI agents — delivering statistical confidence at 83% less cost than fixed-N trial approaches. 52 pages, 5 figures, 660+ tests.
For an in-depth look at AgentAssay's capabilities, evidence, and enterprise context:
View Full Case Study on varunpratap.comPart of the Qualixar research platform — building open tools for reliable AI agent development.
Token-efficient agent testing
Behavioral fingerprinting extracts compact representations of agent actions — tool sequences, state transitions, and decision patterns — instead of comparing raw text outputs. These low-dimensional signals require fewer samples to detect behavioral changes.
AgentAssay runs a small calibration set (5-10 runs), measures behavioral variance per scenario, and computes the minimum number of trials needed for a target confidence level. High-variance scenarios receive more trials; stable scenarios receive fewer.
Coverage metrics, contract checks, metamorphic relations, and mutation analysis can run on production traces already collected — at zero additional token cost. This eliminates redundant agent re-execution.
AgentAssay is a research initiative from the Qualixar platform, focused on making AI agent testing statistically rigorous and cost-effective. It complements other Qualixar initiatives like SuperLocalMemory (agent memory) and SkillFortify (agent security).
Built by Varun Pratap Bhardwaj · A Qualixar Research Initiative
