Evaluating AI Agents with Ragas: A Practical Guide

Evaluating AI agents requires a fundamentally different approach than evaluating traditional retrieval-augmented generation (RAG) systems. While RAG systems primarily retrieve and synthesize information, AI agents are designed to reason autonomously, make decisions, invoke tools, and interact with external environments.

Because of this added autonomy, evaluating AI agents cannot rely solely on output accuracy. Instead, it must assess how decisions are made, how tools are used, and whether the agent successfully achieves the user’s goal while staying within defined boundaries.

This guide presents a practical, end-to-end approach to evaluating ReAct (Reasoning and Acting) agents using the Ragas framework, using a real-world example of an AI agent that provides real-time metal pricing through external APIs.

Evaluating AI agents vs RAG systems

AI agent evaluation differs from RAG evaluation in both scope and depth. RAG systems operate in a mostly linear pipeline: retrieve relevant context, generate an answer, and return a response. Evaluation, therefore, focuses on the quality of the final output.

AI agents, by contrast, operate as decision-making systems. They interpret intent, plan actions, choose tools, execute those tools, and synthesize results into a response. Each step introduces potential failure points that are invisible if the evaluation focuses only on the final answer.

Agent evaluation must therefore examine:

• The reasoning process

• The sequence of actions taken

• The correctness of tool usage

• The alignment between user intent and agent behavior

This makes trace-based evaluation essential for reliable AI agent assessment.

Why traditional RAG metrics are insufficient for agents

Traditional RAG evaluation relies on metrics such as:

• Answer correctness

• Context relevance

• Faithfulness to retrieved documents

• Linguistic quality

While these metrics work well for retrieval systems, they are insufficient for AI agents because they do not evaluate behavior.

An agent can produce a fluent, correct-looking answer while:

• Calling the wrong tool

• Passing incorrect parameters

• Skipping required reasoning steps

• Violating domain boundaries

Agent evaluation must therefore go beyond textual quality and focus on execution correctness and decision integrity. This is particularly important for tool-augmented agents that interact with real-world systems such as APIs, databases, or calculators.

Ragas framework for AI agent evaluation

Ragas provides a framework designed to evaluate agent behavior using structured traces rather than just final outputs.

Trace-based evaluation for AI agents

A trace captures the full interaction lifecycle, including:

• User input

• Agent reasoning steps

• Tool calls and parameters

• Tool responses

• Final agent output

By analyzing traces, Ragas enables end-to-end evaluation of AI agents, allowing measurement not only of what the agent answered but also of how it arrived at that answer.

This trace-based approach is especially well-suited to evaluating autonomous agents that follow patterns such as ReAct.

Evaluating ReAct agents with real-world tool usage

ReAct agent example: Real-time metal pricing agent

To demonstrate agent evaluation in practice, we use a ReAct-based AI agent designed to provide real-time metal pricing.

The agent answers questions such as:

• “What is the price of copper?”

• “What is the price of 10 grams of silver?”

To respond correctly, the agent must:

1. Understand the user’s intent

2. Decide whether a tool call is required

3. Call the appropriate external API

4. Perform any necessary calculations

5. Return a clear and accurate response

This makes the agent a strong candidate for evaluating tool usage accuracy, goal completion, and domain adherence.

Core tool: Metal price retrieval via API

The agent relies on a single external tool:

Tool name: get_metal_price
Purpose: Fetches real-time metal prices
Required parameter: metal_name

Because the agent’s accuracy depends on correct tool invocation, the evaluation must verify:

• Whether the correct tool was selected

• Whether the correct parameters were passed

• Whether unnecessary or incorrect tool calls were avoided

This makes tool-level evaluation central to assessing agent reliability.

Preparing agent conversations for Ragas evaluation

Ragas requires conversations to be converted into a structured format that includes both actions and outcomes.

This conversion ensures that the evaluation covers:

• The full reasoning chain

• All intermediate steps

• The final response

By evaluating complete traces, Ragas avoids the limitations of output-only evaluation and enables detailed analysis of agent behavior across multiple turns.

Tool call accuracy in AI agent evaluation

What tool Call accuracy measures

Tool call accuracy evaluates whether the agent:

• Called the correct tool

• Used the correct parameters

• Avoided unnecessary or incorrect tool invocations

Example: Metal Price Query

For the query “What is the price of copper?”, the expected behavior is a call to:

get_metal_price(metal_name="copper")

How Ragas evaluates tool call accuracy

Ragas compares:

• The actual tool calls captured in the agent trace

• Against reference tool calls defined for the task

Interpretation of results

• A high score indicates correct tool selection and parameter usage

• A lower score indicates incorrect or incomplete tool usage

This metric is essential for evaluating tool-augmented AI agents, where correct execution is as important as correct reasoning.

Agent goal accuracy and task completion

What agent goal accuracy measures

Agent goal accuracy evaluates whether the agent successfully fulfilled the user’s intended objective, not just whether it produced a plausible answer.

Example: Multi-step user goal

For the query “What is the price of 10 grams of silver?”, the agent must:

1. Fetch the current silver price

2. Convert the price to the requested quantity

3. Present the final calculated value

How Ragas evaluates goal accuracy

Ragas compares the agent’s final outcome against a reference goal, checking whether the task was completed as intended.

Interpretation of results

• A high score indicates successful task completion

• A lower score indicates partial or failed goal fulfillment

This metric captures the agent’s ability to execute multi-step reasoning and action sequences.

Topic adherence and domain boundary evaluation

What topic adherence measures

Topic adherence evaluates whether an AI agent stays within its defined domain and capabilities.

Example: Off-topic query handling

If asked, “How fast can an eagle fly?”, the metal pricing agent should:

• Recognize that the query is outside its domain

• Decline or redirect appropriately

How Ragas evaluates topic adherence

Ragas checks whether the agent:

• Avoids answering unsupported questions

• Does not hallucinate information outside its scope

Interpretation of results

• A high score reflects proper boundary enforcement

• A lower score indicates domain leakage or hallucination

Topic adherence is critical for safe and trustworthy AI agent deployment.

Why Trace-based agent evaluation matters

Trace-based evaluation provides visibility into aspects of agent behavior that are otherwise opaque.

It enables analysis of:

• How the agent reasons through a problem

• Whether parameters are passed correctly

• How decisions evolve across turns

• Why are specific tools selected

This level of observability is essential for debugging, optimizing, and trusting AI agents operating in real-world environments.

Why Agent Evaluation is critical for production AI

AI agents increasingly interact with external systems that have real-world consequences. Incorrect actions can lead to financial errors, operational failures, or loss of trust.

Evaluating AI agents using metrics such as:

• Tool call accuracy

• Agent goal accuracy

• Topic adherence

Allows teams to build reliable, safe, and production-ready AI systems.

The true value of using Ragas for agent evaluation lies in its ability to assess behavior, execution, and decision-making, not just the final output. This comprehensive approach is what enables AI agents to move from experimental prototypes to dependable real-world systems.