Agentic AI Explained: The Next Evolution of Artificial Intelligence

Welcome to the Sentient Code, where intelligence is engineered, autonomy is emerging, and a line between human and machine grows thinner. Each episode, we decode the algorithms, explore the robotics, and examine the ideas shaping the future of artificial minds.

Welcome back to the deep Dive. Today's Tuesday, February seventeenth, twenty twenty six, and honestly, staring at that date on the calendar, I'm having a bit of a where did the time go? Moment? Right, But it's more than that, it's a how did the world change this fast? Moment?

It's disorienting, isn't it. The velocity of the last twenty four months specifically has been unlike anything we've seen in tech history.

I mean really, I was actually digging through my digital archives this morning, you know, looking at some old notes from late twenty twenty two, early twenty twenty three. Do you remember the initial chat GPT craze vividly? Oh?

Absolutely, the wow factor of a computer writing a haikup or a little poem.

Right, we were all losing our minds because a machine could summarize an email or write a wedding toast. It felt like absolute magic.

It did.

But looking back from where we sit right now in early twenty twenty six, it feels a little bit like looking at a telegraph machine.

It does. It seems almost quaint because those systems, as impressive as they were, they were fundamentally passive.

I'm passive. That's the word.

They were waiting for you. You are the pilot. They were just the engine. If you stop typing, they stopped thinking exactly.

You had to prompt engineer your way to a result, step by step by step. But today, today we are talking about the shift that makes that era look like the Stone Age. We're talking about the realization that AI isn't just a tool you talk to anymore. It's a tool that does things. It is moved from chat to act.

That is the critical distinction. That is the whole ballgame. We have left the era conversation and entered the era of agency.

So today we're doing a deep dive into agentic AI. And I want to be clear right off the bat, this isn't just another buzzword we're throwing around.

No, it's very real.

If you've been following the industry analysis, you know that Gardner officially declared twenty twenty six the year of agentic AI.

And that's a significant declaration. You know, they don't just throw those titles around lightly.

Well, I have to play Devil's advocate for a second here, Gardner says a lot of things. In twenty twenty three, they were hyping the metaverse pretty hard, and we all saw how that played out.

That's a fair point, a very fair point.

Is this just another marketing slogan to sell enterprise software or is there actually something different happening in the silicon.

It is fundamentally different. And the proof isn't just in the marketing. It's in the deployment numbers. It's in the actual usage. The statistic that jumped out at me from the research stack you sent over is that by the end of this year or so by the end of twenty twenty six, forty percent of all enterprise applications will embed task specific AI agents.

Forty percent.

That can be forty four zero. Now, to put that in perspective, twelve months ago that number was less than five percent.

Wow.

That is not a trend line. That is a vertical wall. It's a step change.

That's adoption at a speed that usually breaks things. That's scary fast.

It is, and the money follows. I mean, the market is surging from about what seven point eight billion dollars sort of projected fifty two billion dollars by twenty thirty. But honestly, forget the dollars for a second. The reason the spending is skyrocketing is utility. The promise of generative AI was I'll help you, right. The promise of a gentic AI is I'll do the work for you.

Okay, So that is our mission today. We are going to cut through the noise. I want to unpack what it actually means to have a digital colleague. And I don't want the brochure version.

Right, Let's get under the hood.

I want to know how these things work. What is physically happening when I give an instruction, Why is this happening now in early twenty twenty six and not two years ago? And frankly, I want to talk about the messiness of it all, because I don't buy that it's all smooth saling.

It is definitely not all smooth sailing, but it is the tipping point where the capability finally caught up to the science fiction.

So let's start with the basics. But let's go deep definitions agentic AI. It sounds academical, little jargoning.

I'd be it.

Yeah, if I'm explaining this to a smart friend who fell into a coma in twenty twenty four and just woke up, what is the core difference between the AI they knew and the agentic AI of twenty twenty six.

The simplest way to frame it is reactive.

Versus proactive, reactive versus proactive.

Traditional generative AI. The stuff from the coma days was reactive. If you asked a question, it predicts the next word. It gives you an answer. Maybe it writes some code, but then it stops. It goes dormant. It has no drive of its own.

It's a super smart encyclopedia, but it doesn't care if the job gets done. It has no skin in the game exactly.

It has no agency. Agentic AI, on the other hand, has a goal. You don't give it a prompt. You give it an objective.

An objective, so not write me an email about but something bigger.

Much bigger, you say, book me a flight to London under eight hundred dollars, or refactor this entire code base to be Python three compliant. Or plan this whole marketing campaign, and the agent figures out the how.

That's the key, right, the how. It's not just filling in the blanks anymore.

Yes, it breaks the high level goal down into steps. It executes those steps. It checks its own work, and this is the most important part. It keeps going until the job is done or it hits a hard wall and needs help.

That checking its own work part is where I want to drill in. Because in the old days, if I asked a model to write code, it would write it. If the code was broken, too bad, yep, your problem. Now I had to copy and paste the error message back in and say, hey, you messed up.

You were the feedback loop.

You were part of the machine, right, I was the debugger.

In an agentic system, the softer is the feedback loop. This brings us to the anatomy of how these things function. It's a pattern that's often referred to as the REACT.

Pattern react ore eACT yes, short.

For reason and act. It's a continuous loop that well, it mimics how a human solves a problem.

Okay, let's make this tangible. I want to walk through this loop. Let's say I am an agent I'm a personal assistant agent. You give me a goal, find out if my favorite band is playing in Chicago this year and get tickets.

Okay, good example.

In the old version, the LM just hallucinates a date. Because it's training data is two years old. It makes something up right.

It confidently tells you, yes, they are playing in twenty twenty one, and you're just frustrated.

So how does the react loop handle this? What's the first thing I the agent do.

The first step is perception. The agent has to gather information from its environment. It's not looking at its internal training data anymore. It needs fresh data, live data.

Well knows it doesn't know precisely.

The reasoning part of its brain says, I don't know the answer to this. I need to use a tool to find out.

A tool, not just its own memory.

Yes, this is crucial. The agent has access to tools. Think of these as APIs. It has a web search tool, it has a ticket tool, it has access to your calendar tool. So step one, it perceives the request.

Okay.

Step two planning. It engages in what we call chain of thought reasoning. It literally talks to itself in the background. It forms a strategy.

What is it saying, like? What does that internal monologue look like?

It's saying, goal, buy tickets for the user's favorite band in Chicago. Step one, I need to know who their favorite band is. Let me check my memory. Ah, it's the Lumineers. Okay. Step two search for the Lumineers tour dates Chicago twenty twenty six. Step three, check users calendar for conflicts on those dates. Step four if free, search for tickets under their price limit. Step five purchase.

It creates a checklist, an actual plan.

Of attack exactly, and then comes the action. It calls the web search tool. It executes that query.

Okay, so I the agent run the search, and let's say the search comes back and says no tour dates listed for Chicago in twenty twenty four of the bot would just say sorry, no dates.

And give up right end of story. But an agent moose to the next phase, observation and adaptation. It looks at the output of the action result no dates in Chicago. It evaluates this against the goal. The goal has not been met, so it iterates.

It tries a different angle, doesn't just quit, yes.

It updates the plan. The reasoning part kicks in again. It might think, Okay, maybe they aren't playing Chicago proper, but what about a nearby city. Let me expand my search radius to one hundred miles. Or maybe the dates just haven't been announced yet. Let me check the band's official Twitter account instead of a general search. That's a better source.

It pivots. Yeah, it problem solves.

It pivots, It runs the loop again, search Twitter, observe result. Ah, I see they're playing a festival in Milwaukee, which is ninety minutes away. New plan. Check if the user is willing to travel, cross reference their travel history. Hmm, they've gone to Milwaukee for concerts before. This is a viable option.

That is a fundamental shift. It feels much more like how I work. Yeah, if I hit a roadblock, I don't just power down. I look for a window. I try something else exactly.

That persistence, that goal seeking behavior is what defines agency.

But I want to clarify the architecture here because I think a lot of people, even tech people, still confuse the agent with the LLM. They think GPT five is the agent.

No, and that's a vital distinction to make. Think of the LLM, the GGT five or the clawed opus model as the brain. The brain, it provides the reasoning, the language, understanding, the logic. But a brain in a jar cannot type an email. A brain in a jar cannot access a database or click a button.

It needs a body, It needs hands, It needs a body.

The agent is the scaffolding around the brain. It includes the tools we mentioned, which are the arms and legs. It includes the sensors for perception, and crucially, it includes memory systems.

This is something that came up a lot, the importance of memory because llms typically have amnesia. Right, yeah, you start a new chat. It has no idea who you are.

They do every time you start a new chat. It's a blank slate. But an agent needs contact. It needs to know what you asked last week. It needs to know your preferences, your constraints.

So how do they solve the amnesia problem? How does it remember?

We use something called vector databases.

Okay, without getting two into the map, weeds visualize that for me. What is that?

Imagine a standard database, like a spreadsheet. You search for the term client X, and it finds the row that says client X. It's a literal keyword match. Simple a vector database is more like a three D map of concepts. When the agent learns something like the user hates early morning flights, it converts that concept into a string of numbers a vector, and places it in a specific spot

So it's storing the meaning, not just the keywords exactly.

So three weeks later, when you say book a trip to Denver, the agent stands its memory map, sees the trip concept, and it looks at what other concepts are nearby in the map, and it finds the vector for hates early morning flights. It retrieves that memory and injects it into the planning step.

So it doesn't just book the six point zero am flight because it's cheapest. It remembers all be cranky and filters that option out precisely.

That long term persistence, that memory is what makes it a colleague, not just a calculator.

So why is this happening now? I remember, back in early twenty twenty three, there was that project called autogpt. Do you remember that?

Oh? Absolutely, everyone was talking about it.

It was the GitHub repo heard round the world. Everyone installed it. It promised to do exactly this, run loops, achieve goals, but let's be honest, it kind of sucked.

It was a fascinating failure, a glorious, very expensive failure. For many people.

It would get stuck in these infinite loops. It would try to Google something, fail, try again, fail, and just burn through your API credits until you went broke.

It was ambitious, but it was premature. In twenty twenty three, the underlying models, the brains, just weren't smart enough. They lacked the sophisticated reasoning capability to recover from errors or to pivot effectively.

So what changed? What were the key breakthroughs in the last say, eighteen months that made twenty twenty six the year of the Agent.

There were three main pillars that all came together. First, the reasoning models themselves got a lot better. Okay, we saw the release of open ais O one series and Thropics, Claude thinking models, and Google's Gemini Advanced. These models were trained specifically to think before they speak. They are significantly better at that planning step of the React loop.

They make better checklist, more robust.

Checklist, much better checklists, and they're less likely to be distracted or go off the rails. Second is context windows.

Right, the amount of information they can hold it once exactly.

We went from a few thousand tokens to models that can hold millions of tokens in their working memory. They can read an entire book or a massive code based instantly and understand it all at once, which.

Gives them the situational awareness they need to make good decisions right.

And the third, and this is geeky, but crucial, is reliable tool calling.

What does that mean?

In twenty twenty three, if you asked a model to use a calculator, it might get the syntax wrong. It might forget a bracket or a comma. It was clumsy. Today the models are fine tuned to interface with software with APIs almost perfectly. They know how to format the request correctly every time.

It's the difference between a toddler trying to use a hammer and a master carpenter using a hammer. The intent was there, but the execution was messy.

That's a perfect analogy, and that reliability is what allowed enterprise adoption to jump from five percent to forty percent. You couldn't put the toddler in charge of your payroll. You can put the carpenter in charge.

Which brings us to the next big evolution. In this story, we've talked about the single agent, the single carpenter, but the industry seems to have realized that one superbot isn't the answer. Right, We're hearing a lot about the multi agent revolution.

That's right. We moved from the solo model to the squad model, from a single agent to a team the squad.

I like that, but play the skeptic for me. Why do I need a squad? If GPT six or whatever it is so smart, why can't it just do it all?

It comes down to what researchers call the generalist curse.

M HM, I unpack that for me.

Imagine you have a brilliant human genius, just an Einstein level intellect. You ask them to write a beautiful poem. They write a great poem. Then you ask them to write a poem, and solve a complex calculus problem and debug a Python script all at the same time.

Their brain melts. They'd be terrible at all.

Three their performance in each individual task drops significantly. This happens to LLLMS two. It's a phenomenon called attention drift. When you ask one model to hold the context for five different disciplines legal coding, creative writing, data analysis, it gets mediocre at all of them. It can't specialize.

So specialization is the fix. Just like with people.

Specialization is the fix. Just like a real company. You don't hire one person to be your lawyer, your coder, and your graphic designer. You hire a team of SAR specialists.

So walk me through how a squad tackles a problem. Let's say we're building a new software feature. How does that work?

Okay, so you have a supervisor agent. This is the boss, the project manager. It takes your high level request build a user authentication page for a new app.

And it doesn't write a single line of code itself, not a line.

Its only job is to break the goal down and delegate.

Who does it call? Who's on the team.

It calls the researcher agent to look up best practices for secure logins. It calls the coder agent, which is fine tuned on code, to write the actual HTML and CSS. But here is the most critical edition. It calls the critic agent.

The critic. I like the sound of that.

The critic's only job is to look at the code the coder wrote and try to break it. To be adversarial. It finds bugs, It checks for security flaws, It says this code is garbage. It doesn't handle this edge case.

Do it again. That is fascinating. We are literally building the creative tension and quality control of a human team into the software itself.

We are we are replicating the address serial nature of human collaboration, and the data shows it works. Google Research published a paper just last month in January twenty twenty six that highlighted this specific dynamic.

What was the key finding?

They found that simply throwing more agents at a problem isn't always better. If you have one hundred agents all shouting at each other in a flat structure, you just get chaos.

Right, too many cooks.

Exactly. The key is smart orchestration. It's about the hierarchy. A small, well orchestrated team with a clear supervisor and critic beats a massive, disorganized mob every single time.

So the manager agent is actually the most valuable piece of code in the stack. Its ability to lead is the real intelligence.

Exactly. The ability to delegate, review, and synthesize is the bottleneck. Just like in a human organization.

Who are the players driving this right now? If I want to hire a squad today, where do I go?

It's the big names you'd expect, but the tools are new. Open Ai has their Frontier platform and the Codex desktop app, which is heavily focused on this orchestration layer. Anthropic just released Claude Opus four point six, which specifically features agent teams that you can spin up with basically one click. You just define the roles.

And for the developers listening who want to build their.

Own, you have open source frameworks like langgraf and crew ai. These are libraries that let you build these custom org charts in code. You define agent A is the researcher, agent B is the writer, and you draw the lines for how information flows between them.

It's like building a circuit board, but the components are intelligent workers.

That's a beautiful way to put it. It's exactly that.

Okay, we've been very abstract for a while. I want to ground this. I want to know what this feels like on it Tuesday morning at my desk.

Let's do it.

The briefing material had a scenario about a Q two sales trip. I want to roleplay this minute by minute, because I think anyone who has ever planned a multi stop business trip knows the specific kind of administrative hell it usually is.

Let's do it's a perfect use case.

Okay, set the scene. It's nine zero zero. Am I need to plan a trip to Europe to visit twelve key accounts in twenty twenty four, I'm opening Expedia, I'm opening Outlook, I'm opening Salesforce. I'm checking visa requirements on some government website. I'm probably crying a little bit.

It's a four hour task easily, and you'll make mistakes easily.

Now, in twenty twenty six, I open my agent interface. What do I type?

You type one single paragraph, something like plan my Q two sales trip to Europe for my twelve key accounts, book flights and hotels, keep the total budget under eight thousand dollars, Schedule meetings with the primary decision makers at each company, and prepare customized proposals for each one. Oh, and flag any potential risks.

Okay, a hit enter, does it just spit out a PDF instantly? What happens next?

No, and this is important. It thinks you might see a little status bar that says planning workflow or assembling team. Then behind the scenes, the squad activates.

Who goes first, who gets the first assignment.

The research agent probably kicks off first. It dives into your CRM. It's pulling up the contact info for the twelve accounts. But it's not just that. It's also going to LinkedIn and Google News. It's building a real time dossier.

What's it looking for?

Things like client A just had a bad earnings call, their budget is tight, they're cost conscious right now. Or client BE just hired a new CTO three weeks ago. She has a background in cloud security.

So it's finding the crucial context that I would have missed or forgotten to look for.

Right Meanwhile, concurrently, the travel agent is fighting with the budget.

Agent, fighting you mean negotiating.

Literally negotiating. The travel agent finds a great direct flight, but it's twelve hundred dollars. The budget agent immediately rejects it and says, no, our hardcat for the whole trip is eight dollars. That flight is inefficient. Find something cheaper.

So it's an internal debate.

Yes, the travel agent iterates, okay, if we fly into Munich instead of Frankfurt and take a one hour train. We save four hundred dollars. The budget agent.

Approves, and the calendar agent what's it doing.

The calendar agent is due the part humans hate most. It's already sending out polite, professional emails to the assistance of those twelve decision makers. Wow, it says, hi, host name will be in town on the fourteenth? Does two point zero pm work for a meeting? It's handling all the time zone math. It's handling the inevitable back and forth of No, he's busy, then how about four point zero pm on the fifteenth. It's a full on negotiation.

And the proposal agent, this.

Is the heavy lifter. It takes the research from that first agent. Client A is cost conscious and it generates a slide deck that emphasizes ROI and long term cost savings. For Client B, the one with the new CTO. It highlights our products advanced security features. It's tailoring the pitch automatically.

And finally, the risk agent, the paranoid.

One, the necessary one. It's scanning the horizon. It's checking for things you'd never think of. Alert there is a planned rail strike in Germany on the fifteenth your travel day. Alert the euro To dollar exchange rate is volatile. This week budget impact could be five percent higher. Flags these things for you.

So let's say an hour goes by, I get a notification on my screen trip planned. I open it up. What do I see?

You see a clean dashboard, You see the full itinerary. You see the draft emails ready to be sent to your contacts. You see links to the customized slide decks, and you see a list of flags for review.

From the risk agent and at the bottom and at.

The bottom a single button approve and execute.

If I hit that button, what happens.

Gay credit card is charged for the flights and hotels, The calendar invites are sent, the files are saved to the right folders. It's done. The whole four hour nightmare is done in sixty minutes, and you spend maybe five of them writing the prompt and reviewing the plan.

That is seductive. I mean the productivity gain there isn't ten percent. It's not even one hundred percent. It's one thousand percent.

That's why companies are scrambling to deploy this. And it's not just for sales trips. We're seeing devon style agents in software development increasing velocity by three to five x.

They're writing and testing their own code exactly.

We're seeing customer service agents resolving a eighty percent of tickets and up selling customers to new plans, all without a human involved.

It sounds perfect. It sounds like we've solved work.

It sounds perfect.

Uh, let's stop you there, because I don't believe in perfect. And that Gartner report, the same one that hyped the Year of the Agent, dropped a very sobering statistic that you pointed out.

You're referring to the failure rate.

Yes, forty percent of agentic projects may be canceled by the end of twenty twenty seven. That is almost a coin flip. If this tech is so amazing, why is nearly half of it destined for the trash heap?

Because agency brings new, much higher stakes risks. When you move from chat to act, the cost of an error just explodes.

Let's talk about that. Hallucinations. We used to laugh about them in twenty twenty three when a bot made up a court case or a fake historical fact. But what happens when an agent hallucinates.

If a chat bot hallucinates, you read it, you roll your eyes, and you say, that's wrong, no harm done. If an agent hallucinates, it books a non refundable first class flight to Sydney, Australia instead of Sydney Nova Scotia. Oh, it transfers ten thousand dollars to the wrong vendor because it misread an invoice. It deletes a production database because it misunderstood the instruction, clean up the test environment.

It's a confident mistake with real world often irreversible.

Consequences exactly, and agents are incredibly confident they will execute a bad plan with one hundred percent conviction. And because they can run autonomously in the background, you might not catch the mistake until the money is gone.

Which leads to the security nightmare. I've heard this term being thrown around shadow agents. It sounds like a spy novel, but I'm guessing it's just Bob in Accounting going rogue.

It is exactly Bob in accounting. Look, these tools are becoming incredibly accessible. Bob is tired of copy pasting data from Excel to SAP all day. It's boring, so he spins up his own custom agent using a simple tool like crewe AI. He gives it his log in credentials to the corporate network. He tells it. Every day at five pm, take this spreadsheet and upload it to this system. Do my work for me.

And now there's an unmonitored, unapproved bot running on the corporate network with Bob's permissions.

A bot that has had no security audit. A bot that might be sending sensitive financial data to a public LMAPI for processing, violating all sorts of data privacy laws. Cybersecurity teams are absolutely terrified. They call it the new shadow it. But it's worse because these things act, they have agency, so.

The IT department is scrambling to even find to inventory these rogue colleagues they are.

And then there's the cost. We said operational costs are down because you save on labor, but we have to talk about cloudshock.

Cloud shock, what's that?

Running a react loop is computationally expensive. Every time the agent thinks, plans, checks its work, or iterates. That is a call to a high end model like GPT five or Opus. That costs money. Right now, if you have a fleet of agents running loops twenty four to seven and one of them gets stuck in a loop, maybe it's trying to solve a problem. It just can't solve. It can burn through ten twenty thousand dollars of compute over a weekend without anyone noticing.

Ouch. So you come in Monday morning to a bill that completely wipes out your quarterly budget.

It's happening. So this all leads to a massive urgent focus on governance. The human in the loop isn't just a nice to have feature. It's a non negotiable safety belt.

You absolutely need that approve and execute button for anything important. You need audit logs to see what it did.

It's the new safety protocol. You wouldn't let an intern have the keys to the company bank account on their first day, you can't let an agent either.

So assuming we can put guardrails on Bob's bot and we can stop the cloud shock with proper monitoring, where is this going. Let's look at the future of work, because if agents are doing the research, the booking, the coding and the writing, what am I doing? What's left?

The shift is happening right now. We are moving from a world of creators to a world of orchestrators or supervisors.

Unpack that distinction for me, creator versus orchestrator.

Over the last thirty years of the knowledge economy, Your value was defined by your output. Could you write the code? Could you design the slide? Could you draft the legal proof? You were the maker, You did the thing right.

Your hands are on the keyboard exactly.

Now. The agents are the makers. They create the raw outputs. So your value shifts up a level. Your value is now can you define the goal with perfect clarity? Can you evaluate the plan the agent proposes? Can you supervise the team of agents in spot when they're going off track?

So the new essential skill is leadership, But it's about leading robots, not people.

And that is a different skill set. It requires extreme clarity and precision. Ambiguity is the enemy of an agent. If you give a vague instruction to a human, they'll probably ask for clarification or use their common sense. If you give a vague instruction to an agent, it will find the most literal and often most disastrous way to interpret it.

So we all have to become prompt engineering project managers. That's the new job in a way.

Yet, yes, the question changes from will agents change everything? To how fast can you learn to lead them effectively?

And looking a bit further out, let's say twenty twenty seven twenty twenty eight. If this trajectory holds, what does the economy even look like?

We expect to see fully autonomous ecosystems. We're talking about ninety percent of B to B buying being intermediated by agents.

Ninety percent. That seems incredibly high.

Think about it. Why would a human procurement officer want to have six phone calls with a human sales rep. It's slow, it involves small talk, it involves human bias and scheduling conflicts.

True, it's inefficient.

In the very near future, my company's buying agent will talk directly to your company's selling agent. They will negotiate pricing based on real time supply and demand data. They will verify compliance with regulations. They will draft and sign a smart contract. They will execute the transfer of money.

So we're talking about trillions of dollars in B to B spend.

Happening conversationally between algorithms. We're talking about fifteen trillion dollars in spend being handled with human means only supervising the process, not executing it.

The global economy becomes a conversation between.

Machines while we sleep or while we do more interesting things.

Hopefully that is a lot to process. It's exciting, but it's also a little alienating.

It is it's a new layer of abstraction over reality, and that always feels strange at first.

So let's wrap this up. If we have to summarize the year of the Agent, it's that we have crossed the rubicon. We aren't going back to dumb chatbots. This is the new reality.

No agents that can plan, act and adapt. That is the new baseline for what we expect from AI.

But the big picture takeaway from ME isn't just about replacement. It's about the friction of adoption. It's about the fact that these things are brilliant, but they are also dangerous if unmanaged. They are powerful new employees that need to be trained and supervised.

It's about partnership, but it's a partnership where you have to be the senior partner. You have to be the vigilant boss, at least for now.

I want to leave our listeners with a thought that struck me while I was reading through the source material on the human and the loop concept. It's a bit of a provocation. Go for it. We talk about how great it is to have these digital colleagues working twenty four to seven. They don't sleep, they don't take breaks, they learn our preferences perfectly. They handle our money. But here's the catch.

Go on.

If the agent is doing the work at light speed, analyzing data and generating plans in seconds, and then it has to pause and wait for you to log on, read the report and hit approve, at what point do you stop being their boss and start becoming their bottleneck.

That is the ultimate question, isn't it.

If your team works twenty four to seven at the speed of light and you work nine to five at the speed of a human, you are the slow link in the chain. You are the friction.

It forces you to up your game or to trust the system and step aside.

Exactly So, tomorrow morning, when you look at your to do list, I want you to ask yourself which part of this requires my soul, my creativity, my judgment, and which part belongs to the agent Because the agent is ready for the.

Handoff and it's just waiting for your instruction.

Thanks for listening. We'll see you next time.