You've probably heard the term AI agent everywhere
lately. It's one of those phrases people love
to drop in articles, LinkedIn posts, and conference
talks. But if you ask five people what it actually
means, you'll probably get seven different answers.
All slightly conflicting. Today I want to cut
through that noise. I'll explain in plain and
practical language what an AI agent really is,
how to recognize one, and more importantly, when
you should use one. And because I believe in
showing, not just telling, I'll walk you through
a live demo of a simple agent that triages my
calendar and my inbox. This tiny agent buys me
back two hours a week, and I'll show you how
to build something similar for yourself. No hype,
no sci -fi, just enough structure that you can
create your first agent today. Welcome to Data
and AI with Mukundan, where you learn AI by building.
Hey, I'm Mukundan. This show is about solving
real problems with small, useful AI, things that
you can actually use today. Each week, we pick
one problem, build a simple workflow or tool,
and talk through the decisions, what to automate,
how to check quality, and how to make it reliable.
If you're a builder, creator, or just AI curious,
you'll leave with the steps that you can copy
tonight. I've spent years working in data roles,
analytics, experimentation, marketing science.
And one thing that I've learned is that I don't
care much for buzzwords. I care about outcomes.
When the idea of AI agents started hitting my
feeds, I noticed two very different camps forming.
The first was the hype team declaring that agents
will replace every kind of work we do. The second
was the skeptics. dismissing agents as nothing
more than a chatbot with a few extra steps as
with most things the truth lies somewhere between
agents are useful when they can actually plan
across multiple steps reliably call tools keep
track of short -term memory and adjust based
on feedback if what you're building can't do
those four things then you don't really have
an agent you just have a fancy auto complete
but if you're looking for a definition that you
can explain to your boss here's the definition
i use Because it passes the explain it to your
boss test. An AI agent is a software that takes
a goal, plans the steps it needs to complete
that goal, uses tools to take action, remembers
what has already happened, and adapts using feedback
until the job is finished. I'll repeat that again.
An AI agent is a software that takes a goal,
plans the steps that it needs to complete that
goal, uses tools to take action, remembers what
has already happened, and adapts using feedback
until the job is finished. That distinction matters
because chatbots reply, but agents execute. Think
of five essential pillars that make this possible.
First, you need a clear goal or task, like scheduled
my podcast prep blog this week. Second, you need
planning. which means breaking that goal into
concrete steps, such as check the calendar, group
tasks, propose time blocks, and send invites.
Third, you need tools and actions, things like
calendar APIs, email connectors, or document
fetches. Fourth, you need memory or state, which
records what's already been done, what failed,
and what to try next. Finally, you need feedback
and control. Critique loops, approval checkpoints,
and retry logic that keep the system safe and
useful. Where agents usually fail is when goals
are vague. That guardrails are missing. Tools
fail without retries or actions aren't locked.
If your success criteria are unclear, your agent
will be unclear too. Now, let's look at what
is an agent versus automation versus an assistant.
It helps to draw a line between three things.
Automation, assistants, and agents. Automation
is a set of fixed steps that run the same way
every time, which is perfect when inputs are
stable and predictable. An assistant like a chatbot
can hold a flexible conversation, but usually
cannot handle multi -step execution. An agent,
on the other hand, is both flexible and action
capable. It can plan, choose tools and iterate
until the task is complete a good litmus test
is this if you remove one of its tool connections
can it still make progress by planning another
way well if the answer is yes you probably have
a real agent the answer is no then what you built
is really just a brittle macro now let's look
at tooling landscape without the buzz let's talk
about the actual tooling stripped of all jargon
At the heart of many agents are planners or controllers.
These might be large language models prompting
themselves in loops or more structured planners
like trees and graphs that map decisions. Agents
rely on connectors to reach into your ecosystem.
That is your email, calendar, Google Drive, Notion,
Slack, databases or the broader web. For memory
or... For memory, agents often use a simple scratchpad
to hold state, a vector database for longer -term
recall or even task graphs to keep track of dependencies.
Safety is non -negotiable. That means running
in sandboxes, using approval steps, limiting
permissions, setting rate limits, and running
dry runs before executing live. A practical rule
is to begin with just one or two tools. tie them
to a single tight success metric. Well, I mean,
as most people would say, just if you can't describe
your journey, if you can't describe your user
journey in three boxes, your input, agent, and
output, then your scope is really too big. Let's
look at some five everyday agent demos. To make
this real, let's look at five everyday agents
that you can build. One is a calendar blocker
agent. which reads your goals, scans your schedule,
proposes time blocks, and asks for your approval
before then going ahead and creating those events.
An email triage event labels messages by intent.
Draft smart replies and queues them for your
review. A podcast prep agent gathers guest bios,
compiles a briefing, and drafts questions so
that you're never unprepared. An inbox to Notion
connector automatically converts emails into
tasks with the due dates so that nothing falls
through the cracks. Finally, a data digest agent
which pulls yesterday's key metrics, flags anomalies,
and drafts a summary for Slack so that your team
gets signal instead of noise. Each of these starts
small but compounds into serious time savings.
lightning round to check our instincts before
we build the live agent. I'll read a statement
say true or false out loud or in your head if
you're on a train and I'll give the answer after
a short pause. An AI agent is just a chatbot
that answers questions true or false. If I remove
one tool and the system can still make progress
by replanning probably looking at a real agent
so I'm going to repeat this one if I remove one
tool and the system can still make progress by
replanning I'm probably looking at a real agent
true or false right the third one is clear success
criteria matter less than good prompts true or
false so clear success criteria matter less than
good prompts fourth one I have for you chatbots
reply agents execute across multiple steps I'll
repeat it chatbots reply agents execute across
multiple steps so is that true or false automation
with fixed steps is best when inputs rarely change
TRF automation with fixed steps is best when
inputs rarely change all right this is I'm going
to do one last one here if a tool disconnect
breaks everything you probably built a brittle
macro not an agent right so if a tool disconnect
breaks everything you probably built a brittle
macro not an ai agent is that true or false so
i'm going to just give the answers in a second
but yeah and here's the answers the first one
where the question was an ai agent is just a
chatbot that answers questions the answer is
false the second one was if i remove one tool
the system can still make progress by replanning.
I'm probably looking at a real agent. This one
was true. The third one was clear success criteria
matter less than good prompts. This one was false.
The fourth one was chatbots reply agents execute
across multiple steps. True. Fifth was automation
with fixed steps is best when inputs rarely change
This is also true And finally if a tool disconnect
breaks everything you probably built a brittle
macro not an agent This is also true So if you
scored six on six That's awesome But if you haven't,
just keep listening and I'll be walking through
AI agents in more depth in the upcoming episodes.
So now I wanted to do a more live build of a
calendar plus email triage agent. Let's build
one together. The goal here is to reclaim two
hours by batching deep work and clearing administrative
noise. Success is defined in two ways. By the
end of the process, I should have two 90 -minute
defocus blocks for work and one 45 -minute admin
block added to my calendar for this week. And
my inbox triage should leave no more than 15
messages unclassified for my top five replies.
The plan looks like this. First, read my goals
for the week from a text note or notion. Next,
scan my calendar for open slots. Then propose
blocks aligned with those goals and ask for my
approval before creating anything. Once approved,
create events with clear descriptions and embedded
checklists. After that, pull my 50 most recent
emails and cluster them by intent. Reply, archive,
delegate or read later. Finally, draft replies
for the most valuable threads and queue them
for review. Guardrails keep the agent safe. Always
do a dry run first. Only create items in a draft
or tentative calendar. And never send emails
without my explicit yes. If the calendar API
fails, the agent should fall back by writing
proposed blocks into a document. The email model's
confidence is too low. It should stop at labeling
without drafting. If you want to use agents at
work, see here's the thing. If your agent doesn't
buy you time or create leverage, it's just a
toy. Let's look at... how we want to roll this
out at work without getting fired. If you want
to use agents at work, it would also depend on
how your company is open with using AI agents.
You can start with your own personal workflow
so that you don't risk production data on day
one. Ship a tiny agent with just one tool so
that the scope is easy to control. And always
add approval steps so that your teammates trust
the process. Log every action so that nothing
feels mysterious. And finally, share a one -page
document that outlines the goal, the scope, and
the outcomes. This builds confidence and shows
that you're solving problems, not creating new
risks. Now it's your turn. I want you to pick
one recurring pain point. Maybe it's batching
your calendar, clearing up your inbox, or prepping
content. Define a success metric that would prove
this is working. Write out a simple five -step
plan. Limit yourself to just... one tool. Then
ship a version 0 .5 today. Remember, the goal
is not perfection, it's just proof. We should
always restrict the problem until it becomes
impossible not to ship. Well, if this episode
helped you, share it with a teammate or a friend
who's drowning in admin work. That's exactly
who benefits from agents first. And stay tuned
because this week we'll be going even deeper
into planning techniques. how to make your agents
reason step by step without wandering off track
all right quick writing round i'm also going
to do a more deeper dry run of my calendar and
inbox but you will see a link to this in the
show notes of how i've done it for my calendar
and inbox then you can also try to replicate
the same here's the plan that I want to do right
this was no email sent no events created because
well safety first but the plan is this 290 minute
deep work blocks on Tuesday and Thursday from
930 to 11 a .m. a single admin sweep on Thursday
From 2 45 to 3 30 and a 30 minute workout on
Wednesday at 12 30 These slots they avoid my
2 p .m. Agent check -ins Leave my 4 to 4 50 p
.m. Focus our intact and won't collide With other
things I have going for me For inbox triage.
I the agent will classify the messages into reply
delegate archive or read later, it will only
draft replies for the top five reply items. Keep
every draft under 100 words and include exactly
one next step. If the confidence drops below
0 .7 on any email, it will label it read later
and explain why in a single sentence. All of
this is logged. and i approve before anything
would go live that's the dry run spec but if
you want to follow along i pasted the templates
in the show notes and calendar proposal approval
checklist and the inbox coach prompt that you
can paste into your own chat gpt let's hit it
Before you go, just wanted to let you know that
I record with Riverside. I'm an affiliate partner
of Riverside. They helped me record this episode
and they have amazing audio quality. So that's
something I just wanted to recommend if you're
ever deciding to do your own podcast. This is
a great platform to record because their editing
features are amazing. Their AI editing is, I
think, next level. Helps you remove all awkward
pauses. gives your sound a strong voice it gives
gives you a strong voice so i would highly recommend
riverside as the reason why i chose to be an
affiliate partner because i actually use the
product and i love it so link to join their platform
is in the show notes and again i'm an affiliate
partner so if you make any purchase i may make
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You can start making money by hosting on rss
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it's Mukundan. If this episode helped you, two
tiny favors that make a huge difference. Rate
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I read every single one and it helps more people
find the show. That's all for this week. Thank
you for joining and I will see you in the next
episode of Data and AI with Mukundan, where you
learn AI by building.