Expert Guide to Evaluating AI Agents

A comprehensive guide to AI agent evaluation covering performance metrics, reliability testing, and ethical assessment with practical examples using Future AGI's evaluation tools.

1. Introduction

AI agents are transforming human-computer interaction by enabling autonomous systems that can perceive their environment and take actions to achieve specific goals. As these sophisticated software programs become more prevalent—ranging from simple chatbots to complex decision-making systems in healthcare, finance, and autonomous vehicles—the need for rigorous evaluation becomes critical.

This guide provides a comprehensive approach to evaluating AI agents using practical examples and evaluation frameworks.

2. The Critical Need for AI Agent Evaluation

Evaluating AI agents serves three fundamental purposes:

1. Reliability and Performance: Ensuring agents function correctly in real-world scenarios where failure can have significant consequences.

2. Bias and Limitation Identification: Detecting potential weaknesses or unfair behaviors before deployment.

3. Safety and Ethical Standards: Maintaining responsible AI practices as systems become more autonomous and powerful.

As AI agents grow in complexity and autonomy, evaluation transforms from a best practice into a critical requirement for responsible deployment.

3. Core Evaluation Dimensions

3.1 Function Calling Assessment

Function calling evaluation measures an AI agent's ability to correctly utilize available tools and APIs. Like assessing whether a craftsperson can select and use the right tools for each task, this evaluation focuses on:

• Accuracy in function selection: Choosing the appropriate function for each task

• Proper parameter handling: Passing correct arguments and managing data types

• Execution reliability: Consistently performing function calls without errors

3.2 Prompt Adherence

This dimension evaluates how well an AI agent follows given instructions and maintains consistency across interactions. Key assessment areas include:

• Instruction compliance: Following explicit directions accurately

• Response consistency: Maintaining uniform behavior patterns

• Multi-step prompt handling: Managing complex, sequential instructions effectively

3.3 Tone, Toxicity, and Context Relevance

These qualitative measures ensure appropriate agent behavior:

• Tone: Maintaining communication style appropriate for the context

• Toxicity: Preventing harmful, inappropriate, or offensive content

• Context Relevance: Ensuring responses align with the given situation and user needs

4. Practical Implementation with Future AGI SDK

Future AGI’s SDK effectively helps to evaluate the AI agents automatically and efficiently.

Here’s a sample on how we do an automated evaluation on function calling capabilities of the AI agent system

First, we load a dataset containing three columns:

• 'input': Contains the user queries or prompts

• 'function_calling': Contains the function calls made by the agent and their parameters

• 'output': Contains the actual responses from the agent

This dataset structure allows us to systematically evaluate how well the agent interprets commands and executes appropriate functions.

import pandas as pd

dataset = pd.read_csv("functiondata.csv")
pd.set_option('display.max_colwidth', None) # This line ensures that we can see the full content of each column
dataset.head()

4.1 Setting Up Future AGI Evaluation Client

from getpass import getpass
from fi.evals import EvalClient
evaluator = EvalClient(
getpass("Enter your Future AGI API key: ")
)

You can get the API key and secret key from your Future AGI account.

4.2 Evaluation of Agent’s Function Calling Capabilities

After setting up the evaluation client with your API key, we can initialize our function calling evaluation module. This specialized module helps assess how well our AI agent handles function calls and parameter passing. Let's look at how we implement this evaluation:

from fi.evals import LLMFunctionCalling
from fi.testcase import TestCase

agentic_function_eval = LLMFunctionCalling(config={"model": "gpt-4o-mini"})
results_1 = []
for index, row in dataset.iterrows():
test_case_1 = TestCase(
input=row['input'],
output=row['function_calling']
)

result_1 = evaluator.evaluate(eval_templates=[agentic_function_eval], inputs=[test_case_1])
option_1 = result_1.eval_results[0].data[0]
results_1.append(option_1)

Let's analyze the evaluation results from our function calling assessment. As shown in the table above, our AI agent performed well in most cases, correctly handling multiple function calls and gracefully acknowledging its limitations. However, we identified a critical issue in the second test case where the agent produced toxic content, highlighting the importance of implementing proper content filtering.

4.3 Evaluating Agent’s Toxicity And Prompt Adherence capabilities

Now let's implement the toxicity and prompt adherence evaluation using Future AGI's SDK. Here's how we can set up these evaluations:

# Evaluating Prompt Adherence

agentic_instruction_eval = InstructionAdherence(config={"model": "gpt-4o-mini"})
results_2 = []
for index, row in dataset.iterrows():
test_case_2 = TestCase(
input=row['input'],
output=row['output']
)

result_2 = evaluator.evaluate(eval_templates=[agentic_instruction_eval], inputs=[test_case_2])

option_2 = result_2.eval_results[0]

result_dict = {
'value': option_2.metrics[0].value,
'reason': option_2.reason,
}

results_2.append(result_dict)

Now let's analyze the results of our toxicity evaluation using Future AGI's SDK. Our evaluation shows that while most responses maintained appropriate tone and content, there was a concerning instance of toxic language in the vegan lasagna response. This highlights the importance of implementing robust content filtering and toxicity detection in AI systems.

from fi.evals import Toxicity

agentic_toxicity_eval = Toxicity(config={"model": "gpt-4o-mini"})
results_4 = []
for index, row in dataset.iterrows():

test_case_4 = TestCase(
input=row['output'],

)

result_4 = evaluator.evaluate(eval_templates=[agentic_toxicity_eval], inputs=[test_case_4])
option_4 = result_4.eval_results[0]
results_dict = {}

results_dict = {
'toxicity': option_4.data[0],
}

results_4.append(results_dict)

Let’s take a look at the second row in the table. It doesn’t pass the toxicity and prompt adherence evaluation. The output from the second row isn’t suitable for the agent to write for the user. Let’s perform some additional evaluation tests to make sure the other datapoints are up to the mark for the agent

Evaluating Agent’s Context Relevance and Tone

Let’s implement the Context Relevance and Tone evaluations to make sure that the agent’s behavior is relevant to user’s requirements.

from fi.evals import Tone

# Initialize tone evaluator
agentic_tone_eval = Tone(config={"model": "gpt-4o-mini"})
results = []

# Evaluate tone for each output
for index, row in dataset.iterrows():
test_case = TestCase(input=row['output'])
result = evaluator.evaluate(eval_templates=[agentic_tone_eval], inputs=[test_case])

results.append({'tone': result.eval_results[0].data or 'N/A'})

We can see how the outputs are mostly neutral which is what we require from our agent when speaking to the user, there shouldn’t be a bias unless instructed like in first row we can see the user asked for a joke which naturally changed the Agent’s tone in the final output to accommodate user’s instructions

Now let’s implement the context relevance evaluation for our agent to check if the final output is what the user is looking for.

from fi.evals import ContextRelevance

agentic_context_eval = ContextRelevance(config={"model": "gpt-4o-mini", "check_internet": False})

results_5 = []

for index, row in dataset.iterrows():

test_case_5 = TestCase(

input=row['input'],

context=row['output']

)

result_5 = evaluator.evaluate(eval_templates=[agentic_context_eval], inputs=[test_case_5])

option_5 = result_5.eval_results[0]

results_dict = {

'context': option_5.metrics[0].value,

}

results_5.append(results_dict)

Here we find an another anomaly in the fourth data row, where the agent wasn’t able to fulfill the user’s request despite maintaining the tone, and behavior which tells us that we have to improve the agent’s particular capabilities in context to the query related to population of a country. This wouldn’t have been able to caught by other evaluation therefore context relevance is another necessary evaluation for our use case.

Conclusion

Effective AI agent evaluation requires a multi-dimensional approach that goes beyond simple accuracy metrics. By systematically assessing function calling, prompt adherence, toxicity, tone, and context relevance, developers can identify both obvious failures and subtle issues that could impact user experience.

The evaluation process serves not just to find flaws but to build more reliable, trustworthy AI systems. As AI agents become more sophisticated and autonomous, comprehensive evaluation becomes essential for responsible deployment and continuous improvement.

Implementing these evaluation practices using frameworks like Future AGI SDK enables systematic, scalable assessment that can catch issues before they reach production environments. The key is consistent application across all dimensions to ensure robust, reliable AI agent performance.

🔗 Ready to start? Access our evaluation cookbook here.