Introduction
articles right here in TDS, we explored the basics of Agentic AI. I’ve been sharing with you some ideas that may aid you navigate via this sea of content material now we have been seeing on the market.
Within the final two articles, we explored issues like:
- The way to create your first agent
- What are instruments and the best way to implement them in your agent
- Reminiscence and reasoning
- Guardrails
- Agent analysis and monitoring
Good! If you wish to examine it, I recommend you have a look at the articles [1] and [2] from the References part.
Agentic AI is among the hottest topics in the mean time, and there are a number of frameworks you possibly can select from. Thankfully, one factor that I’ve seen from my expertise studying about brokers is that these basic ideas could be carried over from one to a different.
For instance, the category Agent from one framework turns into chat in one other, and even one thing else, however often with comparable arguments and the exact same goal of connecting with a Giant Language Mannequin (LLM).
So let’s take one other step in our studying journey.
On this submit, we are going to learn to create multi-agent groups, opening alternatives for us to let AI carry out extra complicated duties for us.
For the sake of consistency, I’ll proceed to make use of Agno as our framework.
Let’s do that.
Multi-Agent Groups
A multi-agent staff is nothing greater than what the phrase means: a staff with a couple of agent.
However why do we want that?
Effectively, I created this easy rule of thumb for myself that, if an agent wants to make use of greater than 2 or 3 instruments, it’s time to create a staff. The explanation for that is that two specialists working collectively will do a lot better than a generalist.
Whenever you attempt to create the “swiss-knife agent”, the chance of seeing issues going backwards is excessive. The agent will turn out to be too overwhelmed with completely different directions and the amount of instruments to cope with, so it finally ends up throwing an error or returning a poor outcome.
However, whenever you create brokers with a single function, they are going to want only one software to resolve that downside, due to this fact growing efficiency and bettering the outcome.
To coordinate this staff of specialists, we are going to use the category Workforce from Agno, which is ready to assign duties to the correct agent.
Let’s transfer on and perceive what we are going to construct subsequent.
Venture
Our undertaking will probably be centered on the social media content material era trade. We are going to construct a staff of brokers that generates an Instagram submit and suggests a picture based mostly on the subject offered by the consumer.
- The consumer sends a immediate for a submit.
- The coordinator sends the duty to the Author
- It goes to the web and searches for that matter.
- The Author returns textual content for the social media submit.
- As soon as the coordinator has the primary outcome, it routes that textual content to the Illustrator agent, so it may possibly create a immediate for a picture for the submit.
Discover how we’re separating the duties very effectively, so every agent can focus solely on their job. The coordinator will be sure that every agent does their work, and they’ll collaborate for an excellent ultimate outcome.
To make our staff much more performant, I’ll limit the topic for the posts to be created about Wine & Nice Meals. This manner, we slender down much more the scope of information wanted from our agent, and we will make its position clearer and extra centered.
Let’s code that now.
Code
First, set up the mandatory libraries.
pip set up agno duckduckgo-search google-genaiCreate a file for atmosphere variables .env and add the wanted API Keys for Gemini and any search mechanism you’re utilizing, if wanted. DuckDuckGo doesn’t require one.
GEMINI_API_KEY="your api key"
SEARCH_TOOL_API_KEY="api key"Import the libraries.
# Imports
import os
from textwrap import dedent
from agno.agent import Agent
from agno.fashions.google import Gemini
from agno.staff import Workforce
from agno.instruments.duckduckgo import DuckDuckGoTools
from agno.instruments.file import FileTools
from pathlib import PathCreating the Brokers
Subsequent, we are going to create the primary agent. It’s a sommelier and specialist in gourmand meals.
- It wants a
titlefor simpler identification by the staff. - The
positiontelling it what its specialty is. - A
descriptionto inform the agent the best way to behave. - The
instrumentsthat it may possibly use to carry out the duty. add_name_to_instructionsis to ship together with the response the title of the agent who labored on that job.- We describe the
expected_output. - The
mannequinis the mind of the agent. exponential_backoffanddelay_between_retriesare to keep away from too many requests to LLMs (error 429).
# Create particular person specialised brokers
author = Agent(
title="Author",
position=dedent("""
You're an skilled digital marketer who makes a speciality of Instagram posts.
You know the way to jot down an enticing, Search engine marketing-friendly submit.
You realize all about wine, cheese, and gourmand meals present in grocery shops.
You're additionally a wine sommelier who is aware of the best way to make suggestions.
"""),
description=dedent("""
Write clear, partaking content material utilizing a impartial to enjoyable and conversational tone.
Write an Instagram caption in regards to the requested {matter}.
Write a brief name to motion on the finish of the message.
Add 5 hashtags to the caption.
In case you encounter a personality encoding error, take away the character earlier than sending your response to the Coordinator.
"""),
instruments=[DuckDuckGoTools()],
add_name_to_instructions=True,
expected_output=dedent("Caption for Instagram in regards to the {matter}."),
mannequin=Gemini(id="gemini-2.0-flash-lite", api_key=os.environ.get("GEMINI_API_KEY")),
exponential_backoff=True,
delay_between_retries=2
)Now, allow us to create the Illustrator agent. The arguments are the identical.
# Illustrator Agent
illustrator = Agent(
title="Illustrator",
position="You're an illustrator who makes a speciality of footage of wines, cheeses, and effective meals present in grocery shops.",
description=dedent("""
Primarily based on the caption created by Marketer, create a immediate to generate an enticing photograph in regards to the requested {matter}.
In case you encounter a personality encoding error, take away the character earlier than sending your response to the Coordinator.
"""),
expected_output= "Immediate to generate an image.",
add_name_to_instructions=True,
mannequin=Gemini(id="gemini-2.0-flash", api_key=os.environ.get("GEMINI_API_KEY")),
exponential_backoff=True,
delay_between_retries=2
)Creating the Workforce
To make these two specialised brokers work collectively, we have to use the category Agent. We give it a reputation and use the argument to find out the kind of interplay that the staff may have. Agno makes accessible the modes coordinate, route or collaborate.
Additionally, don’t neglect to make use of share_member_interactions=True to be sure that the responses will stream easily among the many brokers. You too can use enable_agentic_context, that permits staff context to be shared with staff members.
The argument monitoring is sweet if you wish to use Agno’s built-in monitor dashboard, accessible at https://app.agno.com/
# Create a staff with these brokers
writing_team = Workforce(
title="Instagram Workforce",
mode="coordinate",
members=[writer, illustrator],
directions=dedent("""
You're a staff of content material writers working collectively to create partaking Instagram posts.
First, you ask the 'Author' to create a caption for the requested {matter}.
Subsequent, you ask the 'Illustrator' to create a immediate to generate an enticing illustration for the requested {matter}.
Don't use emojis within the caption.
In case you encounter a personality encoding error, take away the character earlier than saving the file.
Use the next template to generate the output:
- Submit
- Immediate to generate an illustration
"""),
mannequin=Gemini(id="gemini-2.0-flash", api_key=os.environ.get("GEMINI_API_KEY")),
instruments=[FileTools(base_dir=Path("./output"))],
expected_output="A textual content named 'submit.txt' with the content material of the Instagram submit and the immediate to generate an image.",
share_member_interactions=True,
markdown=True,
monitoring=True
)Let’s run it.
# Immediate
immediate = "Write a submit about: Glowing Water and sugestion of meals to accompany."
# Run the staff with a job
writing_team.print_response(immediate)That is the response.
That is how the textual content file seems like.
- Submit
Elevate your refreshment recreation with the effervescence of glowing water!
Overlook the sugary sodas, and embrace the crisp, clear style of bubbles.
Glowing water is the last word palate cleanser and a flexible companion for
your culinary adventures.
Pair your favourite glowing water with gourmand delights out of your native
grocery retailer.
Attempt these pleasant duos:
* **For the Traditional:** Glowing water with a squeeze of lime, served with
creamy brie and crusty bread.
* **For the Adventurous:** Glowing water with a splash of cranberry,
alongside a pointy cheddar and artisan crackers.
* **For the Wine Lover:** Glowing water with a touch of elderflower,
paired with prosciutto and melon.
Glowing water is not only a drink; it is an expertise.
It is the right approach to take pleasure in these particular moments.
What are your favourite glowing water pairings?
#SparklingWater #FoodPairing #GourmetGrocery #CheeseAndWine #HealthyDrinks
- Immediate to generate a picture
A vibrant, eye-level shot inside a gourmand grocery retailer, showcasing a range
of glowing water bottles with numerous flavors. Organize pairings round
the bottles, together with a wedge of creamy brie with crusty bread, sharp cheddar
with artisan crackers, and prosciutto with melon. The lighting must be shiny
and welcoming, highlighting the textures and colours of the meals and drinks.After now we have this textual content file, we will go to no matter LLM we like higher to create photos, and simply copy and paste the Immediate to generate a picture.
And here’s a mockup of how the submit can be.
Fairly good, I’d say. What do you assume?
Earlier than You Go
On this submit, we took one other step in studying about Agentic AI. This matter is sizzling, and there are a lot of frameworks accessible out there. I simply stopped making an attempt to study all of them and selected one to begin truly constructing one thing.
Right here, we have been in a position to semi-automate the creation of social media posts. Now, all now we have to do is select a subject, regulate the immediate, and run the Workforce. After that, it’s all about going to social media and creating the submit.
Definitely, there may be extra automation that may be completed on this stream, however it’s out of scope right here.
Concerning constructing brokers, I like to recommend that you just take the simpler frameworks to begin, and as you want extra customization, you possibly can transfer on to LangGraph, for instance, which permits you that.
Contact and On-line Presence
In case you preferred this content material, discover extra of my work and social media in my web site:
GitHub Repository
https://github.com/gurezende/agno-ai-labs
References
[1. Agentic AI 101: Starting Your Journey Building AI Agents] https://towardsdatascience.com/agentic-ai-101-starting-your-journey-building-ai-agents/
[2. Agentic AI 102: Guardrails and Agent Evaluation] https://towardsdatascience.com/agentic-ai-102-guardrails-and-agent-evaluation/
[3. Agno] https://docs.agno.com/introduction
[4. Agno Team class] https://docs.agno.com/reference/groups/staff
