You've probably heard the term AI agent a hundred
times this year. Everyone's talking about it.
But lately, a new phrase has started showing
up. Multi -agent systems. Sounds technical, but
it's actually pretty simple. A multi -agent system
is just a group of AI agents that talk to each
other, share tasks, and work together like a
team. Instead of one agent doing everything alone,
Multiple agents handle different parts of a job
and pass the results between them. In today's
episode, we're going to unpack what that means.
I'll show you how multi -agent systems work,
how they communicate, what can go wrong, and
how you can use them to save real time in your
daily work. We'll even walk through a live example
of agents that research, write, and review content
for you, all while you stay in control. By the
end of this episode, you'll understand what separates
basic automation from real collaboration between
AI systems. Welcome back to Data and AI with
Mukundan, where you learn AI by building. I'm
Mukundan, and in this show, we talk about practical
human ways to use AI tools, not for hype, but
for real results. Now, in the last step... two
episodes we talked about how single agents can
plan your day or even manage your job hunt but
in real life work doesn't happen in isolation
you don't have one person who does everything
you have a group of people who collaborate hand
things off check each other's work and make sure
the final result is strong and that's exactly
what multi -agent systems are they are like your
wife virtual team instead of one large model
doing all the work you break the process into
smaller career steps each agent gets a job each
agent passes its results to the next one today
i'm going to explain this in plain english and
show you how to think about building your own
ai team one that can work while you sleep So
let's start with the basics. A single agent is
like a solo worker. You give it a goal, it plans
the steps, it acts and it learns from feedback.
A multi -agent system is like a small team though.
It's like a group of agents, each one good at
a specific job. They can communicate with each
other, divide the work, check each other's output
and hand tasks back and forth. Now, let's say
you wanted to write a research article. You could
assign the first agent to gather information
from trusted sources. The second agent could
write the first draft. The third could edit it
for clarity. The fourth could format it and get
ready to post. So basically, each agent has a
narrow focus. One plans, one writes, one reviews.
They don't try to do everything at once. They
just do one part well and pass the baton. So
the real magic happens when they share context.
That's what makes it feel like teamwork instead
of just random automation. Here's how agents
communicate. They don't talk like humans. They
pass structured messages, small packets of information
that tell the next agent what's done and what's
next. Now imagine three agents working on a short
report. The first one says, summarize the latest
data on renewable energy. The second replies,
summary complete. Here are the top five insights.
The third looks at those insights, finds a gap
and sends a note back. It says, double check
insight number three. So the second one reviews
it really and fixes the error and returns it.
This back and forth is called message passing.
It keeps everyone aligned. Now each agent knows
what's been done, what's pending, and what to
correct. That simple exchange is what allows
multi -agent systems to coordinate just like
human teams. There's accountability because each
step is logged. There's reliability because if
one fails, the others can pick up where it left
off. And there's transparency. because you can
always trace why something happened when the
structure is missing agents start stepping on
each other's toes when it's there the system
feels stable and intelligent now let's take a
real world example remember the job hunting agent
we built in the last episode now let's make it
a multi -agent here's how that would look in
practice the first agent tracks to job applications
every time you apply it adds the details the
company role company the role date salary and
link to your notion or google sheet the second
agent does the research it looks up each company
checks their glassdoor ratings recent funding
and team size then it sends that data to the
next agent The third agent prepares interview
questions based on that information. It tailors
them to the company and your resume. The fourth
agent handles follow -ups. After the interview,
it drafts a thank you email and sets a reminder
to follow up if you don't hear back. Now each
agent knows what the others are doing. When you
apply to a new company, the tracker alerts the
researcher. The researcher passes notes to the
prep agent. The prep agent then alerts the follow
-up agent once the interview is complete. The
result is a system that behaves like an organized
invisible team working behind the scenes. You
still review everything, you're still the decision
maker, but the heavy lifting is off of your plate.
Now let's do another live demo where we have
a simple multi -agent workflow. that can run
on top of any LLM model. We'll build three agents,
a researcher, a writer and a reviewer. The goal
is to create a 200 -word LinkedIn post about
three new AI tools that launched this week. The
researcher starts by searching for the newest
tools. It collects short descriptions and features.
Writer then receives that input and drafts a
clean, readable post summarizing the key takeaways.
Reviewer checks that tone, corrects mistakes,
and shortens the text where needed. You can watch
them communicate. The researcher then sends also,
here's what I found. Writer replies, draft complete.
The reviewer comments, shorten paragraph 2, fix
typos, rephrase, conclusion. Writer updates and
resubmits. now within a minute you have a finished
piece that's fact -checked clear and ready to
post that's what a multi -functioning multi -agent
system feels like something that's functioning
at least well it doesn't rush right so it coordinates
it's the digital version of a well -run project
team of course things don't always go smoothly
multi -agent systems fail when there's no hierarchy
No memory or weak feedback loops. If every agent
thinks it's in charge, they'll overwrite each
other's work. If they forget what's already been
done, they'll repeat the same task again. If
there's no feedback, one small error multiplies
as it moves through the chain. That's why guardrails
matter. Always define who leads, who follows,
and how feedback flows. The easiest way is to
start with a single shared goal, two or three
agents, and a rule for when humans get to approve.
It's not about making machines work alone. It's
about making them work with you, predictably
and safely. Now, let's pause for a quick quiz
to see if you're catching the pattern. I'll read
five statements out loud. You can just say true
or false in your head. One, multi -agent systems
are just faster versions of single agents. I'll
repeat that again. Multi -agent systems are just
faster versions of single agents. And I'll just
say the answer as well simultaneously. So the
answer is false because multi -agent systems
aren't faster, they're just smarter. Two, agents
need... Clear communication to work together
effectively Do is true communication is really
everything Because the question is agents need
clear communication to work together effectively
and that is true three feedback loops only matter
for solo agents Feedback loops only matter for
solo agents and this is false because feedback
loops matter even more when multiple agents are
involved 4. Multi -agent systems work best when
every agent has the same goal. 4 is false because
they work best when each agent has a distinct
role. Again, the question was multi -agent systems
work best when every agent has the same goal.
They work best when each agent has a distinct
role. 5. Human approval steps prevent chaos.
human approval steps prevent chaos five is true
because the human in the loop checkpoints keep
everything grounded so this one was true all
right so now let's make this real for you here's
your challenge for the week pick one workflow
in your life that always takes too long something
with at least two steps then imagine what it
would like look like if two simple agents handled
those steps and talk to each other. For example,
if you have one agent summarize your meeting
notes, then the second agent could turn those
summaries into action items. You don't even have
to build it yet. Just describe how they would
pass information, what they would need to remember
and where would you step in for approval. That's
how you start thinking like a system designer.
Once you can imagine how the pieces fit together,
Building it is easy. Now, before we wrap up,
I wanted to leave you with one thought. Automation
saves you minutes. Collaboration saves you hours.
Single agents are great at doing one task well.
Multi -agent systems are powerful because they
think ahead, hand off work, and close the loop.
If this episode helped you understand how to
design those systems, Share it with one teammate
who's experimenting with AI workflows. Now next
week, we'll go one level deeper into something
new. How to combine human decisions with agent
actions. It's about learning how to supervise
and delegate to your AI like a real manager.
Until then, remember this. The best AI systems
don't just work for you. They work with you.