Agentic AI 103: Building Multi-Agent Teams

Introduction

articles right here in TDS, we explored the basics of Agentic AI. I’ve been sharing with you some ideas that may enable you to navigate via this sea of content material we now have been seeing on the market.

Within the final two articles, we explored issues like:

Find out how to create your first agent
What are instruments and implement them in your agent
Reminiscence and reasoning
Guardrails
Agent analysis and monitoring

Good! If you wish to examine it, I recommend you take a look at the articles [1] and [2] from the References part.

Agentic AI is likely one of the hottest topics for the time being, and there are a number of frameworks you may select from. Happily, one factor that I’ve seen from my expertise studying about brokers is that these elementary ideas may 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 normally with related arguments and the exact same goal of connecting with a Massive Language Mannequin (LLM).

So let’s take one other step in our studying journey.

On this put up, we’ll discover ways 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 multiple agent.

However why do we’d like that?

Properly, 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 rationale for that is that two specialists working collectively will do a lot better than a generalist.

If 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 totally different directions and the amount of instruments to take care of, so it finally ends up throwing an error or returning a poor outcome.

Alternatively, while you create brokers with a single objective, they may want only one software to resolve that drawback, due to this fact growing efficiency and bettering the outcome.

To coordinate this staff of specialists, we’ll use the category Group from Agno, which is ready to assign duties to the right agent.

Let’s transfer on and perceive what we’ll construct subsequent.

Mission

Our challenge will probably be targeted on the social media content material technology business. We are going to construct a staff of brokers that generates an Instagram put up and suggests a picture based mostly on the subject supplied by the person.

The person sends a immediate for a put up.
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 put up.
As soon as the coordinator has the primary outcome, it routes that textual content to the Illustrator agent, so it will probably create a immediate for a picture for the put up.

Workflow of the Group of brokers. Picture by the writer.

Discover how we’re separating the duties very effectively, so every agent can focus solely on their job. The coordinator will ensure that every agent does their work, and they’re going to collaborate for a superb last outcome.

To make our staff much more performant, I’ll limit the topic for the posts to be created about Wine & Advantageous 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 targeted.

Let’s code that now.

Code

First, set up the mandatory libraries.

pip set up agno duckduckgo-search google-genai

Create a file for setting 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 Group
from agno.instruments.duckduckgo import DuckDuckGoTools
from agno.instruments.file import FileTools
from pathlib import Path

Creating the Brokers

Subsequent, we’ll create the primary agent. It’s a sommelier and specialist in gourmand meals.

It wants a identify for simpler identification by the staff.
The position telling it what its specialty is.
A description to inform the agent behave.
The instruments that it will probably use to carry out the duty.
add_name_to_instructions is to ship together with the response the identify of the agent who labored on that job.
We describe the expected_output.
The mannequin is the mind of the agent.
exponential_backoff and delay_between_retries are to keep away from too many requests to LLMs (error 429).

# Create particular person specialised brokers
author = Agent(
    identify="Author",
    position=dedent("""
                You might be an skilled digital marketer who focuses on Instagram posts.
                You understand how to write down an enticing, Web optimization-friendly put up.
                You already know all about wine, cheese, and gourmand meals present in grocery shops.
                You might be additionally a wine sommelier who is aware of  make suggestions.
                
                """),
    description=dedent("""
                Write clear, participating content material utilizing a impartial to enjoyable and conversational tone.
                Write an Instagram caption concerning the requested {matter}.
                Write a brief name to motion on the finish of the message.
                Add 5 hashtags to the caption.
                If you happen to 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 concerning 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(
    identify="Illustrator",
    position="You might be an illustrator who focuses on photos of wines, cheeses, and fantastic meals present in grocery shops.",
    description=dedent("""
                Primarily based on the caption created by Marketer, create a immediate to generate an enticing photograph concerning the requested {matter}.
                If you happen to 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 Group

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 could have. Agno makes accessible the modes coordinate, route or collaborate.

Additionally, don’t overlook to make use of share_member_interactions=True to ensure that the responses will stream easily among the many brokers. It’s also possible to use enable_agentic_context, that allows staff context to be shared with staff members.

The argument monitoring is good 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 = Group(
    identify="Instagram Group",
    mode="coordinate",
    members=[writer, illustrator],
    directions=dedent("""
                        You're a staff of content material writers working collectively to create participating 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.
                        If you happen to 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 'put up.txt' with the content material of the Instagram put up and the immediate to generate an image.",
    share_member_interactions=True,
    markdown=True,
    monitoring=True
)

Let’s run it.

# Immediate
immediate = "Write a put up about: Glowing Water and sugestion of meals to accompany."

# Run the staff with a job
writing_team.print_response(immediate)

That is the response.

Picture of the Group’s response. Picture by the writer.

That is how the textual content file seems to be like.

- Submit
Elevate your refreshment sport with the effervescence of glowing water! 
Overlook the sugary sodas, and embrace the crisp, clear style of bubbles. 
Glowing water is the final 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.
Strive these pleasant duos:

*   **For the Basic:** 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 proper solution to get pleasure from 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 variety
of glowing water bottles with varied flavors. Prepare 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 ought to be vivid 
and alluring, highlighting the textures and colours of the meals and drinks.

After we now 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 put up could be.

Mockup of the put up generated by the Multi-agent staff. Picture by the writer.

Fairly good, I’d say. What do you suppose?

Earlier than You Go

On this put up, we took one other step in studying about Agentic AI. This matter is sizzling, and there are numerous frameworks accessible available in the market. I simply stopped attempting to be taught all of them and selected one to start out really constructing one thing.

Right here, we had been capable of semi-automate the creation of social media posts. Now, all we now have to do is select a subject, regulate the immediate, and run the Group. After that, it’s all about going to social media and creating the put up.

Definitely, there may be extra automation that may be finished on this stream, however it’s out of scope right here.

Relating to constructing brokers, I like to recommend that you simply take the better frameworks to start out, and as you want extra customization, you may transfer on to LangGraph, for instance, which permits you that.