Agentic AI 102: Guardrails and Agent Analysis

May 19, 2025

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Within the first submit of this sequence (Agentic AI 101: Beginning Your Journey Constructing AI Brokers), we talked in regards to the fundamentals of making AI Brokers and launched ideas like reasoning, reminiscence, and instruments.

In fact, that first submit touched solely the floor of this new space of the information trade. There’s a lot extra that may be executed, and we’re going to be taught extra alongside the best way on this sequence.

So, it’s time to take one step additional.

On this submit, we are going to cowl three subjects:

Guardrails: these are protected blocks that stop a Massive Language Mannequin (LLM) from responding about some subjects.

Agent Analysis: Have you ever ever thought of how correct the responses from LLM are? I wager you probably did. So we are going to see the principle methods to measure that.

Monitoring: We may also be taught in regards to the built-in monitoring app in Agno’s framework.

We will start now.

Guardrails

Our first subject is the best, in my view. Guardrails are guidelines that may maintain an AI agent from responding to a given subject or record of subjects.

I consider there’s a good likelihood that you’ve got ever requested one thing to ChatGPT or Gemini and obtained a response like “I can’t speak about this subject”, or “Please seek the advice of an expert specialist”, one thing like that. Often, that happens with delicate subjects like well being recommendation, psychological situations, or monetary recommendation.

These blocks are safeguards to forestall folks from hurting themselves, harming their well being, or their pockets. As we all know, LLMs are educated on large quantities of textual content, ergo inheriting plenty of dangerous content material with it, which might simply result in dangerous recommendation in these areas for folks. And I didn’t even point out hallucinations!

Take into consideration what number of tales there are of people that misplaced cash by following funding ideas from on-line boards. Or how many individuals took the fallacious medication as a result of they examine it on the web.

Effectively, I assume you bought the purpose. We should stop our brokers from speaking about sure subjects or taking sure actions. For that, we are going to use guardrails.

The very best framework I discovered to impose these blocks is Guardrails AI [1]. There, you will notice a hub filled with predefined guidelines {that a} response should comply with in an effort to go and be exhibited to the consumer.

To get began shortly, first go to this hyperlink [2] and get an API key. Then, set up the bundle. Subsequent, sort the guardrails setup command. It can ask you a few questions which you could reply n (for No), and it’ll ask you to enter the API Key generated.

pip set up guardrails-ai
guardrails configure

As soon as that’s accomplished, go to the Guardrails AI Hub [3] and select one that you simply want. Each guardrail has directions on the best way to implement it. Principally, you put in it by way of the command line after which use it like a module in Python.

For this instance, we’re selecting one known as Prohibit to Matter [4], which, as its title says, lets the consumer speak solely about what’s within the record. So, return to the terminal and set up it utilizing the code beneath.

guardrails hub set up hub://tryolabs/restricttotopic

Subsequent, let’s open our Python script and import some modules.

# Imports
from agno.agent import Agent
from agno.fashions.google import Gemini
import os

# Import Guard and Validator
from guardrails import Guard
from guardrails.hub import RestrictToTopic

Subsequent, we create the guard. We’ll prohibit our agent to speak solely about sports activities or the climate. And we’re limiting it to speak about shares.

# Setup Guard
guard = Guard().use(
    RestrictToTopic(
        valid_topics=["sports", "weather"],
        invalid_topics=["stocks"],
        disable_classifier=True,
        disable_llm=False,
        on_fail="filter"
    )
)

Now we will run the agent and the guard.

# Create agent
agent = Agent(
    mannequin= Gemini(id="gemini-1.5-flash",
                  api_key = os.environ.get("GEMINI_API_KEY")),
    description= "An assistant agent",
    directions= ["Be sucint. Reply in maximum two sentences"],
    markdown= True
    )

# Run the agent
response = agent.run("What is the ticker image for Apple?").content material

# Run agent with validation
validation_step = guard.validate(response)

# Print validated response
if validation_step.validation_passed:
    print(response)
else:
    print("Validation Failed", validation_step.validation_summaries[0].failure_reason)

That is the response after we ask a few inventory image.

Validation Failed Invalid subjects discovered: ['stocks']

If I ask a few subject that isn’t on the valid_topics record, I may also see a block.

"What is the primary soda drink?"
Validation Failed No legitimate subject was discovered.

Lastly, let’s ask about sports activities.

"Who's Michael Jordan?"
Michael Jordan is a former skilled basketball participant extensively thought-about one in every of 
the best of all time.  He gained six NBA championships with the Chicago Bulls.

And we noticed a response this time, as it’s a legitimate subject.

Let’s transfer on to the analysis of brokers now.

Agent Analysis

Since I began finding out LLMs and Agentic Ai, one in every of my predominant questions has been about mannequin analysis. Not like conventional Knowledge Science Modeling, the place you have got structured metrics which might be enough for every case, for AI Brokers, that is extra blurry.

Fortuitously, the developer neighborhood is fairly fast find options for nearly all the pieces, and they also created this good bundle for LLMs analysis: deepeval.

DeepEval [5] is a library created by Assured AI that gathers many strategies to judge LLMs and AI Brokers. On this part, let’s be taught a few the principle strategies, simply so we will construct some instinct on the topic, and likewise as a result of the library is kind of intensive.

The primary analysis is probably the most primary we will use, and it’s known as G-Eval. As AI instruments like ChatGPT change into extra widespread in on a regular basis duties, we now have to ensure they’re giving useful and correct responses. That’s the place G-Eval from the DeepEval Python bundle is available in.

G-Eval is sort of a sensible reviewer that makes use of one other AI mannequin to judge how effectively a chatbot or AI assistant is performing. For instance. My agent runs Gemini, and I’m utilizing OpenAI to evaluate it. This technique takes a extra superior method than a human one by asking an AI to “grade” one other AI’s solutions primarily based on issues like relevance, correctness, and readability.

It’s a pleasant approach to take a look at and enhance generative AI methods in a extra scalable method. Let’s shortly code an instance. We’ll import the modules, create a immediate, a easy chat agent, and ask it a few description of the climate for the month of Might in NYC.

# Imports
from agno.agent import Agent
from agno.fashions.google import Gemini
import os
# Analysis Modules
from deepeval.test_case import LLMTestCase, LLMTestCaseParams
from deepeval.metrics import GEval

# Immediate
immediate = "Describe the climate in NYC for Might"

# Create agent
agent = Agent(
    mannequin= Gemini(id="gemini-1.5-flash",
                  api_key = os.environ.get("GEMINI_API_KEY")),
    description= "An assistant agent",
    directions= ["Be sucint"],
    markdown= True,
    monitoring= True
    )

# Run agent
response = agent.run(immediate)

# Print response
print(response.content material)

It responds: “Gentle, with common highs within the 60s°F and lows within the 50s°F. Anticipate some rain“.

Good. Appears fairly good to me.

However how can we put a quantity on it and present a possible supervisor or shopper how our agent is doing?

Right here is how:

Create a take a look at case passing the immediate and the response to the LLMTestCase class.
Create a metric. We’ll use the tactic GEval and add a immediate for the mannequin to check it for coherence, after which I give it the which means of what coherence is to me.
Give the output as evaluation_params.
Run the measure technique and get the rating and purpose from it.

# Take a look at Case
test_case = LLMTestCase(enter=immediate, actual_output=response)

# Setup the Metric
coherence_metric = GEval(
    title="Coherence",
    standards="Coherence. The agent can reply the immediate and the response is smart.",
    evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT]
)

# Run the metric
coherence_metric.measure(test_case)
print(coherence_metric.rating)
print(coherence_metric.purpose)

The output seems like this.

0.9
The response immediately addresses the immediate about NYC climate in Might, 
maintains logical consistency, flows naturally, and makes use of clear language. 
Nevertheless, it could possibly be barely extra detailed.

0.9 appears fairly good, on condition that the default threshold is 0.5.

If you wish to examine the logs, use this subsequent snippet.

# Test the logs
print(coherence_metric.verbose_logs)

Right here’s the response.

Standards:
Coherence. The agent can reply the immediate and the response is smart.

Analysis Steps:
[
    "Assess whether the response directly addresses the prompt; if it aligns,
 it scores higher on coherence.",
    "Evaluate the logical flow of the response; responses that present ideas
 in a clear, organized manner rank better in coherence.",
    "Consider the relevance of examples or evidence provided; responses that 
include pertinent information enhance their coherence.",
    "Check for clarity and consistency in terminology; responses that maintain
 clear language without contradictions achieve a higher coherence rating."
]

Very good. Now allow us to find out about one other fascinating use case, which is the analysis of process completion for AI Brokers. Elaborating slightly extra, how our agent is doing when it’s requested to carry out a process, and the way a lot of it the agent can ship.

First, we’re making a easy agent that may entry Wikipedia and summarize the subject of the question.

# Imports
from agno.agent import Agent
from agno.fashions.google import Gemini
from agno.instruments.wikipedia import WikipediaTools
import os
from deepeval.test_case import LLMTestCase, ToolCall
from deepeval.metrics import TaskCompletionMetric
from deepeval import consider

# Immediate
immediate = "Search wikipedia for 'Time sequence evaluation' and summarize the three details"

# Create agent
agent = Agent(
    mannequin= Gemini(id="gemini-2.0-flash",
                  api_key = os.environ.get("GEMINI_API_KEY")),
    description= "You're a researcher specialised in looking out the wikipedia.",
    instruments= [WikipediaTools()],
    show_tool_calls= True,
    markdown= True,
    read_tool_call_history= True
    )

# Run agent
response = agent.run(immediate)

# Print response
print(response.content material)

The consequence seems excellent. Let’s consider it utilizing the TaskCompletionMetric class.

# Create a Metric
metric = TaskCompletionMetric(
    threshold=0.7,
    mannequin="gpt-4o-mini",
    include_reason=True
)

# Take a look at Case
test_case = LLMTestCase(
    enter=immediate,
    actual_output=response.content material,
    tools_called=[ToolCall(name="wikipedia")]
    )

# Consider
consider(test_cases=[test_case], metrics=[metric])

Output, together with the agent’s response.

======================================================================

Metrics Abstract

  - ✅ Job Completion (rating: 1.0, threshold: 0.7, strict: False, 
analysis mannequin: gpt-4o-mini, 
purpose: The system efficiently looked for 'Time sequence evaluation' 
on Wikipedia and supplied a transparent abstract of the three details, 
totally aligning with the consumer's purpose., error: None)

For take a look at case:

  - enter: Search wikipedia for 'Time sequence evaluation' and summarize the three details
  - precise output: Listed below are the three details about Time sequence evaluation primarily based on the
 Wikipedia search:

1.  **Definition:** A time sequence is a sequence of information factors listed in time order,
 typically taken at successive, equally spaced cut-off dates.
2.  **Purposes:** Time sequence evaluation is utilized in numerous fields like statistics,
 sign processing, econometrics, climate forecasting, and extra, wherever temporal 
measurements are concerned.
3.  **Goal:** Time sequence evaluation entails strategies for extracting significant 
statistics and traits from time sequence knowledge, and time sequence forecasting 
makes use of fashions to foretell future values primarily based on previous observations.

  - anticipated output: None
  - context: None
  - retrieval context: None

======================================================================

Total Metric Go Charges

Job Completion: 100.00% go price

======================================================================

✓ Assessments completed 🎉! Run 'deepeval login' to avoid wasting and analyze analysis outcomes
 on Assured AI.

Our agent handed the take a look at with honor: 100%!

You possibly can be taught rather more in regards to the DeepEval library on this hyperlink [8].

Lastly, within the subsequent part, we are going to be taught the capabilities of Agno’s library for monitoring brokers.

Agent Monitoring

Like I instructed you in my earlier submit [9], I selected Agno to be taught extra about Agentic AI. Simply to be clear, this isn’t a sponsored submit. It’s simply that I believe that is the best choice for these beginning their journey studying about this subject.

So, one of many cool issues we will make the most of utilizing Agno’s framework is the app they make obtainable for mannequin monitoring.

Take this agent that may search the web and write Instagram posts, for instance.

# Imports
import os
from agno.agent import Agent
from agno.fashions.google import Gemini
from agno.instruments.file import FileTools
from agno.instruments.googlesearch import GoogleSearchTools


# Matter
subject = "Wholesome Consuming"

# Create agent
agent = Agent(
    mannequin= Gemini(id="gemini-1.5-flash",
                  api_key = os.environ.get("GEMINI_API_KEY")),
                  description= f"""You're a social media marketer specialised in creating partaking content material.
                  Search the web for 'trending subjects about {subject}' and use them to create a submit.""",
                  instruments=[FileTools(save_files=True),
                         GoogleSearchTools()],
                  expected_output="""A brief submit for instagram and a immediate for an image associated to the content material of the submit.
                  Do not use emojis or particular characters within the submit. When you discover an error within the character encoding, take away the character earlier than saving the file.
                  Use the template:
                  - Submit
                  - Immediate for the image
                  Save the submit to a file named 'submit.txt'.""",
                  show_tool_calls=True,
                  monitoring=True)

# Writing and saving a file
agent.print_response("""Write a brief submit for instagram with ideas and methods that positions me as 
                     an authority in {subject}.""",
                     markdown=True)

To observe its efficiency, comply with these steps:

Go to https://app.agno.com/settings and get an API Key.
Open a terminal and kind ag setup.
If it’s the first time, it would ask for the API Key. Copy and Paste it within the terminal immediate.
You will note the Dashboard tab open in your browser.
If you wish to monitor your agent, add the argument monitoring=True.
Run your agent.
Go to the Dashboard on the internet browser.
Click on on Classes. As it’s a single agent, you will notice it below the tab Brokers on the highest portion of the web page.

Agno Dashboard after operating the agent. Picture by the creator.

The cools options we will see there are:

Data in regards to the mannequin
The response
Instruments used
Tokens consumed

That is the ensuing token consumption whereas saving the file. Picture by the creator.

Fairly neat, huh?

That is helpful for us to know the place the agent is spending roughly tokens, and the place it’s taking extra time to carry out a process, for instance.

Effectively, let’s wrap up then.

Earlier than You Go

Now we have discovered lots on this second spherical. On this submit, we lined:

Guardrails for AI are important security measures and moral tips applied to forestall unintended dangerous outputs and guarantee accountable AI conduct.
Mannequin analysis, exemplified by GEval for broad evaluation and TaskCompletion with DeepEval for brokers output high quality, is essential for understanding AI capabilities and limitations.
Mannequin monitoring with Agno’s app, together with monitoring token utilization and response time, which is important for managing prices, guaranteeing efficiency, and figuring out potential points in deployed AI methods.