1. Introduction

Recently, the use of AI Agents has been increasing across various industries. Moving beyond simple chatbots that answer questions, they are evolving into forms that can autonomously assess situations, utilize necessary tools, and derive results. However, for these agents to operate as safely and accurately as expected, systematic evaluation, verification, and continuous monitoring are essential.This article introduces the necessary evaluation, verification, and monitoring methods for safely operating AI Agents, along with specific examples of how these can be applied in real business environments.

2. Understanding AI Agents

2.1 Definition and Characteristics of AI Agents

AI Agents are software systems that receive input from the environment or users, make autonomous decisions, and achieve specific goals. Recently, combined with large language models (LLMs), they can understand context and automatically handle complex tasks by calling external tools (e.g., APIs, databases, internal systems).

Key characteristics :

1. Autonomy: Beyond immediately responding to user queries, they directly perform additional actions necessary for problem-solving.
2. Tool utilization ability: Calls appropriate APIs, databases, and analysis tools as needed to gather necessary information.
3. Continuous learning and updating: Continuously improves performance by reflecting user feedback and new data.

2.2 General Operation Process

1. Receive user request
2. Understand context through LLM, etc.
3. Identify necessary tools (e.g., news search API, analysis API)
4. Call tools
5. Aggregate results and deliver to user
6. Collect feedback and retrain (optional)

2.3 Use Cases

• Customer support: Automated FAQ responses, problem-solving guide provision
• Marketing and sales support: Market trend and competitor analysis, report generation
• Data analysis: Search for articles or papers on specific topics, summarize and derive insights
• Business decision support: Generate materials necessary for decision-making by synthesizing quantitative and qualitative data

3. Importance of AI Agent Evaluation, Verification, and Monitoring

3.1 Need for Evaluation and Verification

AI agents go through a much more complex decision-making process than existing static models. If judgment errors or unnecessary tool usage accumulate, problems such as increased costs, information leakage, and decreased work efficiency can occur. Therefore, beyond simple accuracy evaluation, comprehensive evaluation and continuous monitoring of safety, efficiency, and security are necessary.

3.2 Key Considerations

• Accuracy: Evaluate the accuracy of answers or analysis results provided by the agent. This is important to prevent the provision of incorrect information1.
• Processing speed and resource usage: Monitor system response time and resource usage (CPU, GPU, memory) to evaluate efficiency.
• Cost and token usage: Analyze the costs and token usage of AI models in API calls and result generation processes to evaluate economic efficiency.
• Security and permission management: Evaluate whether the agent can be controlled to not call tools inappropriately. Protect sensitive information and maintain system integrity.
• Tool usage efficiency: Evaluate how effectively the agent utilizes external tools or APIs. Measure through API call success rate, duplicate call rate, and value of results compared to call cost (ROI).

4. Evaluation and Verification Workflow and Metric Design

4.1 Setting Goals and Metrics