Introducing the CLASSic Framework: A Holistic Approach to Evaluating Enterprise AI Agents

Aisera’s New Benchmarking Framework for Measuring Real-World Effectiveness of AI Agents

Aisera, a leading provider of Agentic AI for enterprises, has completed a research study that introduces a new benchmarking framework for evaluating the performance of AI agents in real-world enterprise applications.

Aisera has announced that the results of this benchmark study have been accepted at the ICLR 2025 Workshop on building trust in Large Language Models (LLMs) and LLM applications. Aisera plans to open-source this benchmark framework to empower the AI community in driving innovation and advancing enterprise AI agents.

The Need for a Holistic Approach to Evaluating AI Agents

Traditional evaluation methods have focused solely on accuracy and fail to capture the breadth of real-world requirements. Many existing academic and industry benchmarks rely on synthetic data from tasks that fail to reflect the complexity of real-world enterprise environments, their diverse nature, and the inherent risks. To ensure dependable and compliant agentic AI solutions, benchmarking frameworks must also capture operational factors such as cost efficiency, latency, stability, and security.

The CLASSic Framework: A Holistic Approach to Evaluating Enterprise AI Agents

To address these challenges, the authors of this study introduced the CLASSic framework – a holistic approach to evaluating enterprise AI agents across five key dimensions:

• Cost: Measures operational expenses, including API usage, token consumption, and infrastructure overhead
• Latency: Assesses end-to-end response times
• Accuracy: Evaluates correctness in selecting and executing workflows
• Stability: Checks consistency and robustness across diverse inputs, domains, and varying conditions
• Security: Assesses resilience against adversarial inputs, prompt injections, and potential data leaks

Domain-Specific Models Show a Clear Advantage

The evaluation shows that specialized domain-specific AI agents outperform in tasks within complex enterprise settings while ensuring high accuracy, more reliability, lower costs, and stronger security. Although AI Agents built directly on general-purpose foundational models may achieve competitive accuracy across domains, they lag in cost, latency, and security, highlighting opportunities for improvement through domain-specific application architectures, including domain fine-tuning and distillation of these LLMs.

Conclusion

The CLASSic framework serves as a pragmatic guide for enterprise AI adoption, as it directly delivers measurable results and insights that are valuable and actionable for today’s enterprises. Enterprises should adopt AI agents that are not just highly accurate, but at the same time cost-effective, stable, and secure for greater long-term value. In the coming months, we will be sharing our code and datasets publicly for wider adoption of this new framework.

FAQs

• What is the CLASSic framework? The CLASSic framework is a holistic approach to evaluating enterprise AI agents across five key dimensions: cost, latency, accuracy, stability, and security.
• Why is the CLASSic framework important? The CLASSic framework is important because it provides a comprehensive evaluation framework for enterprise AI agents, considering not only accuracy but also operational factors such as cost, latency, and security.
• How does the CLASSic framework address the limitations of traditional evaluation methods? The CLASSic framework addresses the limitations of traditional evaluation methods by considering multiple dimensions, including cost, latency, and security, to provide a more comprehensive understanding of the performance of AI agents.
• What are the key benefits of using the CLASSic framework? The key benefits of using the CLASSic framework include improved accuracy, reliability, cost-effectiveness, and security, as well as a more comprehensive understanding of the performance of AI agents.
• How can I get access to the CLASSic framework? Aisera plans to open-source the CLASSic framework in the coming months, providing the code and datasets publicly for wider adoption.