Academic

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

arXiv:2603.02601v1 Announce Type: new Abstract: Autonomous AI agents are deployed at unprecedented scale, yet no principled methodology exists for verifying that an agent has not regressed after changes to its prompts, tools, models, or orchestration logic. We present AgentAssay, the first token-efficient framework for regression testing non-deterministic AI agent workflows, achieving 78-100% cost reduction while maintaining rigorous statistical guarantees. Our contributions include: (1) stochastic three-valued verdicts (PASS/FAIL/INCONCLUSIVE) grounded in hypothesis testing; (2) five-dimensional agent coverage metrics; (3) agent-specific mutation testing operators; (4) metamorphic relations for agent workflows; (5) CI/CD deployment gates as statistical decision procedures; (6) behavioral fingerprinting that maps execution traces to compact vectors, enabling multivariate regression detection; (7) adaptive budget optimization calibrating trial counts to behavioral var

V
Varun Pratap Bhardwaj · · 1 min read · 16 views
#cs.AI #cs.SE

arXiv:2603.02601v1 Announce Type: new Abstract: Autonomous AI agents are deployed at unprecedented scale, yet no principled methodology exists for verifying that an agent has not regressed after changes to its prompts, tools, models, or orchestration logic. We present AgentAssay, the first token-efficient framework for regression testing non-deterministic AI agent workflows, achieving 78-100% cost reduction while maintaining rigorous statistical guarantees. Our contributions include: (1) stochastic three-valued verdicts (PASS/FAIL/INCONCLUSIVE) grounded in hypothesis testing; (2) five-dimensional agent coverage metrics; (3) agent-specific mutation testing operators; (4) metamorphic relations for agent workflows; (5) CI/CD deployment gates as statistical decision procedures; (6) behavioral fingerprinting that maps execution traces to compact vectors, enabling multivariate regression detection; (7) adaptive budget optimization calibrating trial counts to behavioral variance; and (8) trace-first offline analysis enabling zero-cost testing on production traces. Experiments across 5 models (GPT-5.2, Claude Sonnet 4.6, Mistral-Large-3, Llama-4-Maverick, Phi-4), 3 scenarios, and 7,605 trials demonstrate that behavioral fingerprinting achieves 86% detection power where binary testing has 0%, SPRT reduces trials by 78%, and the full pipeline achieves 100% cost savings through trace-first analysis. Implementation: 20,000+ lines of Python, 751 tests, 10 framework adapters.

Executive Summary

AgentAssay, a token-efficient framework for regression testing non-deterministic AI agent workflows, offers a promising solution to the pressing issue of verifying AI agent stability. By leveraging stochastic three-valued verdicts, agent coverage metrics, and behavioral fingerprinting, AgentAssay achieves significant cost reduction while maintaining rigorous statistical guarantees. The authors' comprehensive approach addresses various aspects of AI agent testing, including mutation testing, metamorphic relations, and CI/CD deployment gates. Experiments demonstrate the effectiveness of AgentAssay in detecting regression, with notable cost savings and improved detection power. This innovative framework has far-reaching implications for the development and deployment of AI agents in various domains.

Key Points

  • ▸ AgentAssay is a token-efficient framework for regression testing non-deterministic AI agent workflows.
  • ▸ AgentAssay achieves 78-100% cost reduction while maintaining rigorous statistical guarantees.
  • ▸ AgentAssay addresses various aspects of AI agent testing, including mutation testing, metamorphic relations, and CI/CD deployment gates.

Merits

Comprehensive Approach

AgentAssay tackles multiple facets of AI agent testing, providing a holistic solution for regression testing.

Significant Cost Reduction

AgentAssay's token-efficient design enables substantial cost savings, making it an attractive option for large-scale AI deployments.

Rigorous Statistical Guarantees

AgentAssay maintains rigorous statistical guarantees, ensuring the reliability and trustworthiness of AI agent testing results.

Demerits

Scalability Challenges

As the size and complexity of AI agent workflows increase, AgentAssay's scalability and performance may be impacted, requiring further optimization and refinement.

Limited Domain Expertise

AgentAssay's effectiveness may depend on the domain-specific knowledge and expertise of the users, potentially limiting its adoption in certain areas.

Technical Complexity

AgentAssay's comprehensive approach and innovative techniques may introduce additional complexity, requiring significant technical expertise and resources to implement and maintain.

Expert Commentary

AgentAssay represents a significant step forward in the development of AI agent testing frameworks, addressing the pressing need for reliable and efficient regression testing. While challenges remain, the authors' comprehensive approach and innovative techniques make AgentAssay an attractive option for researchers, developers, and practitioners alike. As the AI landscape continues to evolve, AgentAssay's impact will be felt across multiple domains, from DevOps and continuous integration to explainability and transparency in AI. Ultimately, AgentAssay's significance lies in its potential to establish a new standard for AI agent testing and validation, promoting the responsible and trustworthy development of AI systems.

Recommendations

  • ✓ Further research and development should focus on optimizing AgentAssay's scalability and performance, particularly in the context of large-scale AI deployments.
  • ✓ The authors should explore potential applications of AgentAssay in emerging domains, such as autonomous vehicles and healthcare, where AI agent testing and validation are critical.

Sources

Related Articles