エージェント技術の習得：AI エージェントの評価

Evaluating an AI model and evaluating an AI agent are related—but they answer fundamentally different questions. A model benchmark tests the capability of a foundation model (how well it understands language, follows instructions, or solves problems on static tasks). An agent evaluation tests the behavior of a system operating end-to-end—planning, calling tools, handling uncertainty, and completing real workflows in a dynamic environment.

This post explains the key differences between model and agent evaluation and walks through five practical tips for evaluating AI agents as production systems. This evaluation approach focuses on trajectories, tools, and outcomes—not just model scores.

What’s the difference between evaluating an AI model and evaluating an AI agent?

While model and agent evaluation are inextricably linked, their technical benchmarks and metrics for success are fundamentally different.

AI model evaluation: The capabilities baseline

Evaluating a model focuses on the foundation model (an LLM, or VLM, for example) in isolation. It measures raw cognitive and linguistic potential using static datasets where the input-to-output mapping is predefined. Teams primarily rely on benchmarks like MMLU for general knowledge, GSM8K for mathematical reasoning, and HumanEval for coding proficiency.

Ultimately, the goal of model evaluation is to answer a single question: “Is this engine powerful enough to understand my instructions and reason through facts?”

AI agent evaluation: The performance trajectory

Agent evaluation shifts the lens to the trajectory: the end-to-end sequence of reasoning, tool calls, and environment observations. An agent might use a top-tier model but fail because it hallucinated a JSON schema for an API or entered an infinite loop after a failed search.

Agent evaluation moves into dynamic environments using the GAIA benchmark for real-world assistance, SWE-bench for resolving GitHub issues, and WebArena for web-based task execution. Technically, this evaluation requires tracking Task Success Rate (TSR) to measure intent resolution, Tool Call Accuracy to ensure precision in function calling, and Trajectory Efficiency to identify redundant steps. While a high MMLU score is a prerequisite, it doesn’t guarantee a reliable agent.

The goal shifts from measuring knowledge to measuring outcomes. The question becomes: “Can this system reliably execute a multistep workflow in a nondeterministic environment?”

How to evaluate an AI agent

This section walks through five practical tips for evaluating an AI agent.

Tip #1: Measure task success, not just accuracy

Model benchmarks such as MMLU, GSM8K, and HumanEval indicate whether an agent’s base model is capable, not whether the agent can complete real tasks in your stack.

For agent evaluation, prioritize TSR:

• Define tasks as intent plus constraints; for example: “Update this record through this API within two tool calls.”

• Measure success only when the agent fully resolves the intent within those constraints.

• Track TSR per scenario (normal, degraded tools, ambiguous instructions) to expose brittleness.

Traditional accuracy on the final answer becomes a secondary diagnostic under TSR.

Tip #2: Evaluate full trajectories, not just final answers

Two agents can provide the same answer while behaving very differently: one uses three precise tool calls, while another thrashes through dozens of irrelevant steps, for example. Final-answer grading treats agents as identical, but production behavior does not.

Instrument your agent to log complete trajectories:

• Plans and subgoals

• All tool calls, parameters, and responses

• ​Intermediate reasoning steps where feasible

• Final answer and side effects (writes, updates)

​Then compute metrics like Trajectory Efficiency (steps/tokens per success), Tool Call Accuracy, and failure mode distribution (plan, tool, environment).

Tip #3: ​Make tool usage a first-class signal

Most production agents succeed or fail based on how they use tools—APIs, databases, search—not on phrasing.

​For each evaluation task, specify expected tool behavior:

• Which tools are allowed or required

• Maximum calls per tool

• Expected schema for each call

​Measure the following to reveal patterns like hallucinated API schemas or overuse of slow, expensive tools:

• Tool selection precision and recall: Were the right tools chosen and the wrong ones avoided?

• ​Schema compliance: Did arguments match expected structure without retries?

Tip #4: Score reasoning quality and efficiency

A correct answer with broken reasoning or excessive steps is costly in compute resources. The following techniques can help reasoning and efficiency together:

• Capture reasoning traces (plans or justification fields) and periodically label them as sound, partially flawed, or incorrect.

• Check that reasoning uses retrieved evidence instead of ignoring it.

• Track tokens, tool calls, and end-to-end latency per successful task.

​Use explicit budgets (for example, “95% of tasks under N tokens and M tool calls”) as constraints when you tune prompts, routing, or retry policies.

Tip #5: Build transparent, customizable evaluation from day one

Rather than retrofit observability, it’s optimal to treat evaluation as part of agent design. ​

Here are some ways to do so from first prototype:

• Log every plan, tool call, and key reasoning step with stable IDs so trajectories are easy to reconstruct.

• Attach labels to trajectories (success/failure, error type, human rating).

• Support both global metrics (TSR, Trajectory Efficiency, Tool Call Accuracy) and those that are use-case-specific (citation coverage for research, for example).

​This approach turns evaluation into a daily development tool so that improvements or vulnerabilities can be caught early.

*Table 1. Key dimensions for thorough evaluation of AI agents*

Get started evaluating AI agents

Reliable agentic systems shift evaluation from static model benchmarks to dynamic, trajectory-aware metrics that reflect how agents behave in real environments. You track outcomes, tool usage, reasoning, and cost together, then wire those signals into your development loop from the start.

NVIDIA NeMo Agent Toolkit is designed to plug into existing agent frameworks and add evaluation, optimization, and observability without a full rebuild. It helps you capture the metrics above—task outcomes, trajectories, and tool calls—so you can iterate with evaluation-driven development.

To learn more, watch the related GTC 2026 session and training lab on demand:

• Evaluation-Driven Development: Best Practices for Building Reliable Agents (GTC session)

• Develop Production Agents with Eval-Driven Design (GTC training lab)