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AgentAssayLIVE
Token-Efficient Agent Testing
AgentAssay is a regression testing framework for AI agents that cuts evaluation costs by 78-100% while maintaining statistical rigor. Using stochastic 3-valued verdicts (pass/fail/uncertain) and adaptive budgeting, it tests agent behavior changes efficiently across any framework.
78-100%
Cost Reduction
10
Framework Adapters
86%
Detection Rate
7,605
Trials Validated
Features
78-100% Cost Reduction
Adaptive budget allocation means you only spend tokens where they matter. SPRT-based early stopping.
10 Framework Adapters
LangGraph, CrewAI, AutoGen, OpenAI Agents, Semantic Kernel, DSPy, LlamaIndex, Haystack, Marvin, custom.
Stochastic 3-Valued Verdicts
Pass, fail, or uncertain — with confidence intervals. No more binary testing of probabilistic systems.
Behavioral Fingerprinting
86% detection rate for agent behavior changes. Track regression across model updates.
5D Coverage Model
Task, capability, context, interaction, and edge-case dimensions for comprehensive testing.
Mutation Testing
Automatically generate agent variants to verify your test suite catches real failures.
Use Cases
Regression testing after model updates
Cost-efficient CI/CD for agent pipelines
Multi-framework agent evaluation
Statistical testing of probabilistic agents
Research Paper
AgentAssay: Token-Efficient Agent Evaluation via Stochastic Verdicts
Varun Pratap Bhardwaj, 2026
Read on arXiv →Licensed under AGPL-3.0