AI agent performance measurement is the systematic process of evaluating an autonomous agent’s effectiveness in achieving its goals. Unlike traditional software with predictable outputs, AI agents are non-deterministic, meaning the same input can produce different results. This requires a more sophisticated approach that goes beyond simple pass/fail tests to assess the quality, efficiency, and cost of an agent’s actions.

To get a complete picture of an agent’s performance, you need to look at three core areas: its ability to complete tasks, its skill in using tools, and its efficiency with resources.

This measures the ultimate outcome of the agent’s work. It’s not just about whether the agent finished, but how well it finished.

• Binary Success Rate: The most straightforward metric. Did the agent achieve the final objective? (e.g., Was the code committed to the repository? Was the user’s question answered correctly?)
• Partial-Credit Scoring: Complex tasks involve multiple steps. This metric gives the agent “points” for completing each sub-task (e.g., planning, coding, testing), providing a more nuanced view of where it succeeds or fails.
• Outcome Quality: This is a qualitative assessment of the final product. For a coding agent, this could be the efficiency and readability of the code. For a research agent, it would be the accuracy and relevance of the information it gathered.

These metrics help you understand if your agent is actually solving the user’s problem effectively.

Most agents rely on external tools (APIs, databases, code interpreters) to accomplish tasks. Measuring how well an agent interacts with these tools is critical for diagnosing issues.

• Tool-Call Fix Rate: This tracks how often a tool call fails and whether the agent can self-correct by retrying with different parameters. A high fix rate indicates resilience.
• Tool Selection Accuracy: Did the agent choose the most appropriate tool for the job from its available options?
• Input Formatting: How often does the agent provide correctly structured inputs (e.g., valid JSON) to the tools it calls? Frequent errors here might point to a flawed prompt.

Assessing tool use helps identify whether failures stem from the agent’s reasoning, the tools themselves, or the instructions you’ve given it.

Agents consume resources—specifically, tokens and time. Without careful monitoring, these costs can spiral, especially as usage scales.

• Cost Per Task: The total monetary cost (e.g., in USD) to complete a single task, calculated from the number and type of tokens used in LLM calls.
• Tokens Per Task: The sum of all input and output tokens consumed. This is a useful proxy for cost and complexity.
• Latency: The total time taken from the initial prompt to the final output. High latency can lead to a poor user experience.

These metrics are essential for ensuring your agent is not just effective, but also economically viable.

Building a system to measure agent performance involves capturing detailed data and creating a clear, high-level summary. The goal is to create a dashboard that a technical leader can understand in 30 seconds.

1. Instrument and Capture Traces First, you need to log everything the agent does. This is known as “tracing.” An agent trace is a detailed, step-by-step record of a single run, including the initial prompt, the agent’s internal “thoughts,” every tool it calls, the results of those calls, and its final output. This raw data is the foundation for all other measurements.

2. Use an Evaluation (Eval) Stack Manually reviewing thousands of traces is impossible. Specialized observability and evaluation platforms (like LangSmith, Arize, or Phoenix Trance) are designed for this. You feed them your traces, and they help automate the analysis. You can write custom “evaluators” that check each trace for specific criteria, such as:

• Does the final output contain a specific keyword?
• Was the execute_code tool called successfully?
• Did an LLM-as-judge rate the final answer as high quality?

3. Define and Score Success Criteria For each task, create a clear rubric for what success looks like. This could be a checklist of required outcomes. Your automated evaluators can then score each run against this rubric, enabling partial-credit scoring. For example, a planner/coder/tester agent might be scored on:

• Plan created (10 points)
• Code written (20 points)
• Code passes linter (10 points)
• Code passes tests (30 points)
• Code committed (30 points)

4. Ship a Leadership Dashboard With the data captured and scored, you can now build a simple dashboard. This dashboard should provide a high-level, at-a-glance view of agent health, focusing on trends over time.

This kind of summary allows a leader to quickly assess performance and spot potential issues (like the rising tool-call failure rate) without getting lost in the details of individual traces.

Evaluating agents is a new and evolving discipline with unique challenges that don’t exist in traditional software testing.

One of the biggest hurdles is non-determinism. An agent might succeed on a task five times in a row and then fail on the sixth with the exact same input. This requires you to test at scale and focus on aggregate success rates rather than individual pass/fail results.

Another challenge is the subjectivity of “quality.” While you can programmatically check if code runs, it’s much harder to automatically determine if it’s well-architected or if a written summary is insightful. This often requires combining automated evaluations with periodic human-in-the-loop review to ensure the agent’s output quality remains high.

When an AI agent acts on behalf of a user, it needs a clear identity and a well-defined set of permissions. Kinde provides the critical user management and authorization layer to ensure your agents operate securely and correctly within your application.

For example, an agent that helps users manage their projects should only be able to access the data for the user who invoked it. Using Kinde, you can issue a secure access token for the user’s session. The agent then includes this token in its API calls, and your backend uses Kinde to verify that the agent has the correct permissions to read or write data for that specific user.

This prevents an agent from accidentally accessing one user’s data while working on a task for another. By managing roles and permissions through Kinde, you can strictly control which tools and actions an agent can perform, adding a crucial layer of security and reliability to your agent-driven product.

• Roles and permissions
• Create an API key