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You've probably heard the term AI agent everywhere

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lately. It's one of those phrases people love

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to drop in articles, LinkedIn posts, and conference

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talks. But if you ask five people what it actually

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means, you'll probably get seven different answers.

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All slightly conflicting. Today I want to cut

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through that noise. I'll explain in plain and

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practical language what an AI agent really is,

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how to recognize one, and more importantly, when

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you should use one. And because I believe in

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showing, not just telling, I'll walk you through

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a live demo of a simple agent that triages my

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calendar and my inbox. This tiny agent buys me

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back two hours a week, and I'll show you how

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to build something similar for yourself. No hype,

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no sci -fi, just enough structure that you can

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create your first agent today. Welcome to Data

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and AI with Mukundan, where you learn AI by building.

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Hey, I'm Mukundan. This show is about solving

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real problems with small, useful AI, things that

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you can actually use today. Each week, we pick

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one problem, build a simple workflow or tool,

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and talk through the decisions, what to automate,

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how to check quality, and how to make it reliable.

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If you're a builder, creator, or just AI curious,

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you'll leave with the steps that you can copy

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tonight. I've spent years working in data roles,

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analytics, experimentation, marketing science.

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And one thing that I've learned is that I don't

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care much for buzzwords. I care about outcomes.

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When the idea of AI agents started hitting my

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feeds, I noticed two very different camps forming.

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The first was the hype team declaring that agents

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will replace every kind of work we do. The second

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was the skeptics. dismissing agents as nothing

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more than a chatbot with a few extra steps as

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with most things the truth lies somewhere between

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agents are useful when they can actually plan

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across multiple steps reliably call tools keep

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track of short -term memory and adjust based

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on feedback if what you're building can't do

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those four things then you don't really have

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an agent you just have a fancy auto complete

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but if you're looking for a definition that you

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can explain to your boss here's the definition

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i use Because it passes the explain it to your

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boss test. An AI agent is a software that takes

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a goal, plans the steps it needs to complete

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that goal, uses tools to take action, remembers

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what has already happened, and adapts using feedback

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until the job is finished. I'll repeat that again.

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An AI agent is a software that takes a goal,

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plans the steps that it needs to complete that

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goal, uses tools to take action, remembers what

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has already happened, and adapts using feedback

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until the job is finished. That distinction matters

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because chatbots reply, but agents execute. Think

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of five essential pillars that make this possible.

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First, you need a clear goal or task, like scheduled

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my podcast prep blog this week. Second, you need

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planning. which means breaking that goal into

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concrete steps, such as check the calendar, group

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tasks, propose time blocks, and send invites.

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Third, you need tools and actions, things like

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calendar APIs, email connectors, or document

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fetches. Fourth, you need memory or state, which

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records what's already been done, what failed,

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and what to try next. Finally, you need feedback

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and control. Critique loops, approval checkpoints,

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and retry logic that keep the system safe and

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useful. Where agents usually fail is when goals

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are vague. That guardrails are missing. Tools

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fail without retries or actions aren't locked.

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If your success criteria are unclear, your agent

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will be unclear too. Now, let's look at what

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is an agent versus automation versus an assistant.

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It helps to draw a line between three things.

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Automation, assistants, and agents. Automation

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is a set of fixed steps that run the same way

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every time, which is perfect when inputs are

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stable and predictable. An assistant like a chatbot

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can hold a flexible conversation, but usually

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cannot handle multi -step execution. An agent,

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on the other hand, is both flexible and action

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capable. It can plan, choose tools and iterate

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until the task is complete a good litmus test

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is this if you remove one of its tool connections

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can it still make progress by planning another

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way well if the answer is yes you probably have

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a real agent the answer is no then what you built

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is really just a brittle macro now let's look

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at tooling landscape without the buzz let's talk

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about the actual tooling stripped of all jargon

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At the heart of many agents are planners or controllers.

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These might be large language models prompting

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themselves in loops or more structured planners

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like trees and graphs that map decisions. Agents

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rely on connectors to reach into your ecosystem.

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That is your email, calendar, Google Drive, Notion,

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Slack, databases or the broader web. For memory

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or... For memory, agents often use a simple scratchpad

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to hold state, a vector database for longer -term

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recall or even task graphs to keep track of dependencies.

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Safety is non -negotiable. That means running

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in sandboxes, using approval steps, limiting

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permissions, setting rate limits, and running

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dry runs before executing live. A practical rule

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is to begin with just one or two tools. tie them

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to a single tight success metric. Well, I mean,

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as most people would say, just if you can't describe

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your journey, if you can't describe your user

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journey in three boxes, your input, agent, and

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output, then your scope is really too big. Let's

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look at some five everyday agent demos. To make

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this real, let's look at five everyday agents

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that you can build. One is a calendar blocker

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agent. which reads your goals, scans your schedule,

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proposes time blocks, and asks for your approval

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before then going ahead and creating those events.

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An email triage event labels messages by intent.

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Draft smart replies and queues them for your

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review. A podcast prep agent gathers guest bios,

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compiles a briefing, and drafts questions so

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that you're never unprepared. An inbox to Notion

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connector automatically converts emails into

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tasks with the due dates so that nothing falls

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through the cracks. Finally, a data digest agent

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which pulls yesterday's key metrics, flags anomalies,

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and drafts a summary for Slack so that your team

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gets signal instead of noise. Each of these starts

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small but compounds into serious time savings.

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lightning round to check our instincts before

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we build the live agent. I'll read a statement

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say true or false out loud or in your head if

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you're on a train and I'll give the answer after

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a short pause. An AI agent is just a chatbot

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that answers questions true or false. If I remove

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one tool and the system can still make progress

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by replanning probably looking at a real agent

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so I'm going to repeat this one if I remove one

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tool and the system can still make progress by

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replanning I'm probably looking at a real agent

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true or false right the third one is clear success

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criteria matter less than good prompts true or

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false so clear success criteria matter less than

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good prompts fourth one I have for you chatbots

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reply agents execute across multiple steps I'll

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repeat it chatbots reply agents execute across

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multiple steps so is that true or false automation

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with fixed steps is best when inputs rarely change

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TRF automation with fixed steps is best when

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inputs rarely change all right this is I'm going

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to do one last one here if a tool disconnect

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breaks everything you probably built a brittle

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macro not an agent right so if a tool disconnect

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breaks everything you probably built a brittle

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macro not an ai agent is that true or false so

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i'm going to just give the answers in a second

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but yeah and here's the answers the first one

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where the question was an ai agent is just a

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chatbot that answers questions the answer is

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false the second one was if i remove one tool

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the system can still make progress by replanning.

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I'm probably looking at a real agent. This one

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was true. The third one was clear success criteria

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matter less than good prompts. This one was false.

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The fourth one was chatbots reply agents execute

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across multiple steps. True. Fifth was automation

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with fixed steps is best when inputs rarely change

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This is also true And finally if a tool disconnect

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breaks everything you probably built a brittle

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macro not an agent This is also true So if you

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scored six on six That's awesome But if you haven't,

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just keep listening and I'll be walking through

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AI agents in more depth in the upcoming episodes.

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So now I wanted to do a more live build of a

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calendar plus email triage agent. Let's build

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one together. The goal here is to reclaim two

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hours by batching deep work and clearing administrative

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noise. Success is defined in two ways. By the

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end of the process, I should have two 90 -minute

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defocus blocks for work and one 45 -minute admin

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block added to my calendar for this week. And

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my inbox triage should leave no more than 15

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messages unclassified for my top five replies.

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The plan looks like this. First, read my goals

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for the week from a text note or notion. Next,

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scan my calendar for open slots. Then propose

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blocks aligned with those goals and ask for my

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approval before creating anything. Once approved,

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create events with clear descriptions and embedded

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checklists. After that, pull my 50 most recent

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emails and cluster them by intent. Reply, archive,

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delegate or read later. Finally, draft replies

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for the most valuable threads and queue them

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for review. Guardrails keep the agent safe. Always

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do a dry run first. Only create items in a draft

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or tentative calendar. And never send emails

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without my explicit yes. If the calendar API

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fails, the agent should fall back by writing

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proposed blocks into a document. The email model's

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confidence is too low. It should stop at labeling

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without drafting. If you want to use agents at

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work, see here's the thing. If your agent doesn't

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buy you time or create leverage, it's just a

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toy. Let's look at... how we want to roll this

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out at work without getting fired. If you want

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to use agents at work, it would also depend on

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how your company is open with using AI agents.

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You can start with your own personal workflow

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so that you don't risk production data on day

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one. Ship a tiny agent with just one tool so

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that the scope is easy to control. And always

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add approval steps so that your teammates trust

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the process. Log every action so that nothing

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feels mysterious. And finally, share a one -page

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document that outlines the goal, the scope, and

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the outcomes. This builds confidence and shows

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that you're solving problems, not creating new

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risks. Now it's your turn. I want you to pick

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one recurring pain point. Maybe it's batching

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your calendar, clearing up your inbox, or prepping

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content. Define a success metric that would prove

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this is working. Write out a simple five -step

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plan. Limit yourself to just... one tool. Then

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ship a version 0 .5 today. Remember, the goal

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is not perfection, it's just proof. We should

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always restrict the problem until it becomes

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impossible not to ship. Well, if this episode

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helped you, share it with a teammate or a friend

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who's drowning in admin work. That's exactly

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who benefits from agents first. And stay tuned

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because this week we'll be going even deeper

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into planning techniques. how to make your agents

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reason step by step without wandering off track

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all right quick writing round i'm also going

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to do a more deeper dry run of my calendar and

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inbox but you will see a link to this in the

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show notes of how i've done it for my calendar

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and inbox then you can also try to replicate

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the same here's the plan that I want to do right

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this was no email sent no events created because

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well safety first but the plan is this 290 minute

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deep work blocks on Tuesday and Thursday from

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930 to 11 a .m. a single admin sweep on Thursday

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From 2 45 to 3 30 and a 30 minute workout on

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Wednesday at 12 30 These slots they avoid my

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2 p .m. Agent check -ins Leave my 4 to 4 50 p

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.m. Focus our intact and won't collide With other

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things I have going for me For inbox triage.

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I the agent will classify the messages into reply

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delegate archive or read later, it will only

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draft replies for the top five reply items. Keep

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every draft under 100 words and include exactly

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one next step. If the confidence drops below

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0 .7 on any email, it will label it read later

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and explain why in a single sentence. All of

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this is logged. and i approve before anything

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would go live that's the dry run spec but if

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you want to follow along i pasted the templates

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in the show notes and calendar proposal approval

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checklist and the inbox coach prompt that you

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can paste into your own chat gpt let's hit it

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Before you go, just wanted to let you know that

00:16:33.409 --> 00:16:37.230
I record with Riverside. I'm an affiliate partner

00:16:37.230 --> 00:16:39.570
of Riverside. They helped me record this episode

00:16:39.570 --> 00:16:43.490
and they have amazing audio quality. So that's

00:16:43.490 --> 00:16:45.269
something I just wanted to recommend if you're

00:16:45.269 --> 00:16:48.570
ever deciding to do your own podcast. This is

00:16:48.570 --> 00:16:51.809
a great platform to record because their editing

00:16:51.809 --> 00:16:55.110
features are amazing. Their AI editing is, I

00:16:55.110 --> 00:16:57.669
think, next level. Helps you remove all awkward

00:16:57.669 --> 00:17:02.820
pauses. gives your sound a strong voice it gives

00:17:02.820 --> 00:17:06.680
gives you a strong voice so i would highly recommend

00:17:06.680 --> 00:17:09.740
riverside as the reason why i chose to be an

00:17:09.740 --> 00:17:11.799
affiliate partner because i actually use the

00:17:11.799 --> 00:17:16.799
product and i love it so link to join their platform

00:17:16.799 --> 00:17:20.019
is in the show notes and again i'm an affiliate

00:17:20.019 --> 00:17:23.700
partner so if you make any purchase i may make

00:17:23.700 --> 00:17:27.500
a small commission from it i also host an rss

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.com Also, I'm an affiliate partner of RSS because

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they have an amazing distribution channel. They

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are able to publish your episodes from RSS into

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Spotify, Apple Podcasts, Amazon Music, Audible,

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Deezer, Pandora, iHeartRadio, and even lets you

00:17:51.490 --> 00:17:54.210
publish your episode with one button into YouTube

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as well. Did I forget to mention that you get

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You can start making money by hosting on rss

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.com even if you have just 10 downloads a month.

00:18:05.049 --> 00:18:07.430
So that's all you need to make money, 10 downloads

00:18:07.430 --> 00:18:09.390
a month. It's a new feature they started and

00:18:09.390 --> 00:18:13.009
it's amazing. So again, link to joinrss .com

00:18:13.009 --> 00:18:15.190
will be in the show notes. I'm also using cyda

00:18:15.190 --> 00:18:20.569
.ai to help with my research. So if you are interested

00:18:20.569 --> 00:18:25.509
in any research for your content or in general

00:18:25.509 --> 00:18:28.019
you want to learn about anything, want to read

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research papers or any kind of thing to improve

00:18:31.519 --> 00:18:34.480
your knowledge, cider .ai is your platform to

00:18:34.480 --> 00:18:37.200
use. So again, I'm an affiliate partner and I'd

00:18:37.200 --> 00:18:41.799
love if you're able to support the show by clicking

00:18:41.799 --> 00:18:49.619
on my affiliate link and joining cider .ai. Hey,

00:18:49.779 --> 00:18:52.160
it's Mukundan. If this episode helped you, two

00:18:52.160 --> 00:18:54.819
tiny favors that make a huge difference. Rate

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the show five stars. On Spotify, you can just

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open the show page and tap the star button. On

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Apple Podcasts, scroll to the bottom of the show

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page and tap five stars. Also, leave a one -to

00:19:05.240 --> 00:19:09.039
-line review on Apple Podcasts. Tell me one takeaway.

00:19:09.359 --> 00:19:11.619
I read every single one and it helps more people

00:19:11.619 --> 00:19:14.180
find the show. That's all for this week. Thank

00:19:14.180 --> 00:19:15.799
you for joining and I will see you in the next

00:19:15.799 --> 00:19:17.940
episode of Data and AI with Mukundan, where you

00:19:17.940 --> 00:19:18.680
learn AI by building.
