Speaker 1: Welcome to the Sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: Welcome back to the deep Dive. Today's Tuesday, February seventeenth,
Speaker 2: twenty twenty six, and honestly, staring at that date on
Speaker 2: the calendar, I'm having a bit of a where did
Speaker 2: the time go? Moment? Right, But it's more than that,
Speaker 2: it's a how did the world change this fast? Moment?
Speaker 3: It's disorienting, isn't it. The velocity of the last twenty
Speaker 3: four months specifically has been unlike anything we've seen in
Speaker 3: tech history.
Speaker 2: I mean really, I was actually digging through my digital
Speaker 2: archives this morning, you know, looking at some old notes
Speaker 2: from late twenty twenty two, early twenty twenty three. Do
Speaker 2: you remember the initial chat GPT craze vividly? Oh?
Speaker 3: Absolutely, the wow factor of a computer writing a haikup
Speaker 3: or a little poem.
Speaker 2: Right, we were all losing our minds because a machine
Speaker 2: could summarize an email or write a wedding toast. It
Speaker 2: felt like absolute magic.
Speaker 3: It did.
Speaker 2: But looking back from where we sit right now in
Speaker 2: early twenty twenty six, it feels a little bit like
Speaker 2: looking at a telegraph machine.
Speaker 3: It does. It seems almost quaint because those systems, as
Speaker 3: impressive as they were, they were fundamentally passive.
Speaker 2: I'm passive. That's the word.
Speaker 3: They were waiting for you. You are the pilot. They
Speaker 3: were just the engine. If you stop typing, they stopped
Speaker 3: thinking exactly.
Speaker 2: You had to prompt engineer your way to a result,
Speaker 2: step by step by step. But today, today we are
Speaker 2: talking about the shift that makes that era look like
Speaker 2: the Stone Age. We're talking about the realization that AI
Speaker 2: isn't just a tool you talk to anymore. It's a
Speaker 2: tool that does things. It is moved from chat to act.
Speaker 3: That is the critical distinction. That is the whole ballgame.
Speaker 3: We have left the era conversation and entered the era
Speaker 3: of agency.
Speaker 2: So today we're doing a deep dive into agentic AI.
Speaker 2: And I want to be clear right off the bat,
Speaker 2: this isn't just another buzzword we're throwing around.
Speaker 3: No, it's very real.
Speaker 2: If you've been following the industry analysis, you know that
Speaker 2: Gardner officially declared twenty twenty six the year of agentic AI.
Speaker 3: And that's a significant declaration. You know, they don't just
Speaker 3: throw those titles around lightly.
Speaker 2: Well, I have to play Devil's advocate for a second here,
Speaker 2: Gardner says a lot of things. In twenty twenty three,
Speaker 2: they were hyping the metaverse pretty hard, and we all
Speaker 2: saw how that played out.
Speaker 3: That's a fair point, a very fair point.
Speaker 2: Is this just another marketing slogan to sell enterprise software
Speaker 2: or is there actually something different happening in the silicon.
Speaker 3: It is fundamentally different. And the proof isn't just in
Speaker 3: the marketing. It's in the deployment numbers. It's in the
Speaker 3: actual usage. The statistic that jumped out at me from
Speaker 3: the research stack you sent over is that by the
Speaker 3: end of this year or so by the end of
Speaker 3: twenty twenty six, forty percent of all enterprise applications will
Speaker 3: embed task specific AI agents.
Speaker 2: Forty percent.
Speaker 3: That can be forty four zero. Now, to put that
Speaker 3: in perspective, twelve months ago that number was less than
Speaker 3: five percent.
Speaker 2: Wow.
Speaker 3: That is not a trend line. That is a vertical wall.
Speaker 3: It's a step change.
Speaker 2: That's adoption at a speed that usually breaks things. That's
Speaker 2: scary fast.
Speaker 3: It is, and the money follows. I mean, the market
Speaker 3: is surging from about what seven point eight billion dollars
Speaker 3: sort of projected fifty two billion dollars by twenty thirty.
Speaker 3: But honestly, forget the dollars for a second. The reason
Speaker 3: the spending is skyrocketing is utility. The promise of generative
Speaker 3: AI was I'll help you, right. The promise of a
Speaker 3: gentic AI is I'll do the work for you.
Speaker 2: Okay, So that is our mission today. We are going
Speaker 2: to cut through the noise. I want to unpack what
Speaker 2: it actually means to have a digital colleague. And I
Speaker 2: don't want the brochure version.
Speaker 3: Right, Let's get under the hood.
Speaker 2: I want to know how these things work. What is
Speaker 2: physically happening when I give an instruction, Why is this
Speaker 2: happening now in early twenty twenty six and not two
Speaker 2: years ago? And frankly, I want to talk about the
Speaker 2: messiness of it all, because I don't buy that it's
Speaker 2: all smooth saling.
Speaker 3: It is definitely not all smooth sailing, but it is
Speaker 3: the tipping point where the capability finally caught up to
Speaker 3: the science fiction.
Speaker 2: So let's start with the basics. But let's go deep
Speaker 2: definitions agentic AI. It sounds academical, little jargoning.
Speaker 3: I'd be it.
Speaker 2: Yeah, if I'm explaining this to a smart friend who
Speaker 2: fell into a coma in twenty twenty four and just
Speaker 2: woke up, what is the core difference between the AI
Speaker 2: they knew and the agentic AI of twenty twenty six.
Speaker 3: The simplest way to frame it is reactive.
Speaker 2: Versus proactive, reactive versus proactive.
Speaker 3: Traditional generative AI. The stuff from the coma days was reactive.
Speaker 3: If you asked a question, it predicts the next word.
Speaker 3: It gives you an answer. Maybe it writes some code,
Speaker 3: but then it stops. It goes dormant. It has no
Speaker 3: drive of its own.
Speaker 2: It's a super smart encyclopedia, but it doesn't care if
Speaker 2: the job gets done. It has no skin in the
Speaker 2: game exactly.
Speaker 3: It has no agency. Agentic AI, on the other hand,
Speaker 3: has a goal. You don't give it a prompt. You
Speaker 3: give it an objective.
Speaker 2: An objective, so not write me an email about but
Speaker 2: something bigger.
Speaker 3: Much bigger, you say, book me a flight to London
Speaker 3: under eight hundred dollars, or refactor this entire code base
Speaker 3: to be Python three compliant. Or plan this whole marketing campaign,
Speaker 3: and the agent figures out the how.
Speaker 2: That's the key, right, the how. It's not just filling
Speaker 2: in the blanks anymore.
Speaker 3: Yes, it breaks the high level goal down into steps.
Speaker 3: It executes those steps. It checks its own work, and
Speaker 3: this is the most important part. It keeps going until
Speaker 3: the job is done or it hits a hard wall
Speaker 3: and needs help.
Speaker 2: That checking its own work part is where I want
Speaker 2: to drill in. Because in the old days, if I
Speaker 2: asked a model to write code, it would write it.
Speaker 2: If the code was broken, too bad, yep, your problem.
Speaker 2: Now I had to copy and paste the error message
Speaker 2: back in and say, hey, you messed up.
Speaker 3: You were the feedback loop.
Speaker 2: You were part of the machine, right, I was the debugger.
Speaker 3: In an agentic system, the softer is the feedback loop.
Speaker 3: This brings us to the anatomy of how these things function.
Speaker 3: It's a pattern that's often referred to as the REACT.
Speaker 2: Pattern react ore eACT yes, short.
Speaker 3: For reason and act. It's a continuous loop that well,
Speaker 3: it mimics how a human solves a problem.
Speaker 2: Okay, let's make this tangible. I want to walk through
Speaker 2: this loop. Let's say I am an agent I'm a
Speaker 2: personal assistant agent. You give me a goal, find out
Speaker 2: if my favorite band is playing in Chicago this year
Speaker 2: and get tickets.
Speaker 3: Okay, good example.
Speaker 2: In the old version, the LM just hallucinates a date.
Speaker 2: Because it's training data is two years old. It makes
Speaker 2: something up right.
Speaker 3: It confidently tells you, yes, they are playing in twenty
Speaker 3: twenty one, and you're just frustrated.
Speaker 2: So how does the react loop handle this? What's the
Speaker 2: first thing I the agent do.
Speaker 3: The first step is perception. The agent has to gather
Speaker 3: information from its environment. It's not looking at its internal
Speaker 3: training data anymore. It needs fresh data, live data.
Speaker 2: Well knows it doesn't know precisely.
Speaker 3: The reasoning part of its brain says, I don't know
Speaker 3: the answer to this. I need to use a tool
Speaker 3: to find out.
Speaker 2: A tool, not just its own memory.
Speaker 3: Yes, this is crucial. The agent has access to tools.
Speaker 3: Think of these as APIs. It has a web search tool,
Speaker 3: it has a ticket tool, it has access to your
Speaker 3: calendar tool. So step one, it perceives the request.
Speaker 2: Okay.
Speaker 3: Step two planning. It engages in what we call chain
Speaker 3: of thought reasoning. It literally talks to itself in the background.
Speaker 3: It forms a strategy.
Speaker 2: What is it saying, like? What does that internal monologue
Speaker 2: look like?
Speaker 3: It's saying, goal, buy tickets for the user's favorite band
Speaker 3: in Chicago. Step one, I need to know who their
Speaker 3: favorite band is. Let me check my memory. Ah, it's
Speaker 3: the Lumineers. Okay. Step two search for the Lumineers tour
Speaker 3: dates Chicago twenty twenty six. Step three, check users calendar
Speaker 3: for conflicts on those dates. Step four if free, search
Speaker 3: for tickets under their price limit. Step five purchase.
Speaker 2: It creates a checklist, an actual plan.
Speaker 3: Of attack exactly, and then comes the action. It calls
Speaker 3: the web search tool. It executes that query.
Speaker 2: Okay, so I the agent run the search, and let's
Speaker 2: say the search comes back and says no tour dates
Speaker 2: listed for Chicago in twenty twenty four of the bot
Speaker 2: would just say sorry, no dates.
Speaker 3: And give up right end of story. But an agent
Speaker 3: moose to the next phase, observation and adaptation. It looks
Speaker 3: at the output of the action result no dates in Chicago.
Speaker 3: It evaluates this against the goal. The goal has not
Speaker 3: been met, so it iterates.
Speaker 2: It tries a different angle, doesn't just quit, yes.
Speaker 3: It updates the plan. The reasoning part kicks in again.
Speaker 3: It might think, Okay, maybe they aren't playing Chicago proper,
Speaker 3: but what about a nearby city. Let me expand my
Speaker 3: search radius to one hundred miles. Or maybe the dates
Speaker 3: just haven't been announced yet. Let me check the band's
Speaker 3: official Twitter account instead of a general search. That's a
Speaker 3: better source.
Speaker 2: It pivots. Yeah, it problem solves.
Speaker 3: It pivots, It runs the loop again, search Twitter, observe result. Ah,
Speaker 3: I see they're playing a festival in Milwaukee, which is
Speaker 3: ninety minutes away. New plan. Check if the user is
Speaker 3: willing to travel, cross reference their travel history. Hmm, they've
Speaker 3: gone to Milwaukee for concerts before. This is a viable option.
Speaker 2: That is a fundamental shift. It feels much more like
Speaker 2: how I work. Yeah, if I hit a roadblock, I
Speaker 2: don't just power down. I look for a window. I
Speaker 2: try something else exactly.
Speaker 3: That persistence, that goal seeking behavior is what defines agency.
Speaker 2: But I want to clarify the architecture here because I
Speaker 2: think a lot of people, even tech people, still confuse
Speaker 2: the agent with the LLM. They think GPT five is
Speaker 2: the agent.
Speaker 3: No, and that's a vital distinction to make. Think of
Speaker 3: the LLM, the GGT five or the clawed opus model
Speaker 3: as the brain. The brain, it provides the reasoning, the language, understanding,
Speaker 3: the logic. But a brain in a jar cannot type
Speaker 3: an email. A brain in a jar cannot access a
Speaker 3: database or click a button.
Speaker 2: It needs a body, It needs hands, It needs a body.
Speaker 3: The agent is the scaffolding around the brain. It includes
Speaker 3: the tools we mentioned, which are the arms and legs.
Speaker 3: It includes the sensors for perception, and crucially, it includes
Speaker 3: memory systems.
Speaker 2: This is something that came up a lot, the importance
Speaker 2: of memory because llms typically have amnesia. Right, yeah, you
Speaker 2: start a new chat. It has no idea who you are.
Speaker 3: They do every time you start a new chat. It's
Speaker 3: a blank slate. But an agent needs contact. It needs
Speaker 3: to know what you asked last week. It needs to
Speaker 3: know your preferences, your constraints.
Speaker 2: So how do they solve the amnesia problem? How does
Speaker 2: it remember?
Speaker 3: We use something called vector databases.
Speaker 2: Okay, without getting two into the map, weeds visualize that
Speaker 2: for me. What is that?
Speaker 3: Imagine a standard database, like a spreadsheet. You search for
Speaker 3: the term client X, and it finds the row that
Speaker 3: says client X. It's a literal keyword match. Simple a
Speaker 3: vector database is more like a three D map of concepts.
Speaker 3: When the agent learns something like the user hates early
Speaker 3: morning flights, it converts that concept into a string of
Speaker 3: numbers a vector, and places it in a specific spot
Speaker 3: on this three D map. That spot might be near
Speaker 3: other concepts like travel preferences or sleep habits, or being grumpy.
Speaker 2: So it's storing the meaning, not just the keywords exactly.
Speaker 3: So three weeks later, when you say book a trip
Speaker 3: to Denver, the agent stands its memory map, sees the
Speaker 3: trip concept, and it looks at what other concepts are
Speaker 3: nearby in the map, and it finds the vector for
Speaker 3: hates early morning flights. It retrieves that memory and injects
Speaker 3: it into the planning step.
Speaker 2: So it doesn't just book the six point zero am
Speaker 2: flight because it's cheapest. It remembers all be cranky and
Speaker 2: filters that option out precisely.
Speaker 3: That long term persistence, that memory is what makes it
Speaker 3: a colleague, not just a calculator.
Speaker 2: So why is this happening now? I remember, back in
Speaker 2: early twenty twenty three, there was that project called autogpt.
Speaker 2: Do you remember that?
Speaker 3: Oh? Absolutely, everyone was talking about it.
Speaker 2: It was the GitHub repo heard round the world. Everyone
Speaker 2: installed it. It promised to do exactly this, run loops,
Speaker 2: achieve goals, but let's be honest, it kind of sucked.
Speaker 3: It was a fascinating failure, a glorious, very expensive failure.
Speaker 3: For many people.
Speaker 2: It would get stuck in these infinite loops. It would
Speaker 2: try to Google something, fail, try again, fail, and just
Speaker 2: burn through your API credits until you went broke.
Speaker 3: It was ambitious, but it was premature. In twenty twenty three,
Speaker 3: the underlying models, the brains, just weren't smart enough. They
Speaker 3: lacked the sophisticated reasoning capability to recover from errors or
Speaker 3: to pivot effectively.
Speaker 2: So what changed? What were the key breakthroughs in the
Speaker 2: last say, eighteen months that made twenty twenty six the
Speaker 2: year of the Agent.
Speaker 3: There were three main pillars that all came together. First,
Speaker 3: the reasoning models themselves got a lot better. Okay, we
Speaker 3: saw the release of open ais O one series and Thropics,
Speaker 3: Claude thinking models, and Google's Gemini Advanced. These models were
Speaker 3: trained specifically to think before they speak. They are significantly
Speaker 3: better at that planning step of the React loop.
Speaker 2: They make better checklist, more robust.
Speaker 3: Checklist, much better checklists, and they're less likely to be
Speaker 3: distracted or go off the rails. Second is context windows.
Speaker 2: Right, the amount of information they can hold it once exactly.
Speaker 3: We went from a few thousand tokens to models that
Speaker 3: can hold millions of tokens in their working memory. They
Speaker 3: can read an entire book or a massive code based
Speaker 3: instantly and understand it all at once, which.
Speaker 2: Gives them the situational awareness they need to make good
Speaker 2: decisions right.
Speaker 3: And the third, and this is geeky, but crucial, is
Speaker 3: reliable tool calling.
Speaker 2: What does that mean?
Speaker 3: In twenty twenty three, if you asked a model to
Speaker 3: use a calculator, it might get the syntax wrong. It
Speaker 3: might forget a bracket or a comma. It was clumsy.
Speaker 3: Today the models are fine tuned to interface with software
Speaker 3: with APIs almost perfectly. They know how to format the
Speaker 3: request correctly every time.
Speaker 2: It's the difference between a toddler trying to use a
Speaker 2: hammer and a master carpenter using a hammer. The intent
Speaker 2: was there, but the execution was messy.
Speaker 3: That's a perfect analogy, and that reliability is what allowed
Speaker 3: enterprise adoption to jump from five percent to forty percent.
Speaker 3: You couldn't put the toddler in charge of your payroll.
Speaker 3: You can put the carpenter in charge.
Speaker 2: Which brings us to the next big evolution. In this story,
Speaker 2: we've talked about the single agent, the single carpenter, but
Speaker 2: the industry seems to have realized that one superbot isn't
Speaker 2: the answer. Right, We're hearing a lot about the multi
Speaker 2: agent revolution.
Speaker 3: That's right. We moved from the solo model to the
Speaker 3: squad model, from a single agent to a team the squad.
Speaker 2: I like that, but play the skeptic for me. Why
Speaker 2: do I need a squad? If GPT six or whatever
Speaker 2: it is so smart, why can't it just do it all?
Speaker 3: It comes down to what researchers call the generalist curse.
Speaker 2: M HM, I unpack that for me.
Speaker 3: Imagine you have a brilliant human genius, just an Einstein
Speaker 3: level intellect. You ask them to write a beautiful poem.
Speaker 3: They write a great poem. Then you ask them to
Speaker 3: write a poem, and solve a complex calculus problem and
Speaker 3: debug a Python script all at the same time.
Speaker 2: Their brain melts. They'd be terrible at all.
Speaker 3: Three their performance in each individual task drops significantly. This
Speaker 3: happens to LLLMS two. It's a phenomenon called attention drift.
Speaker 3: When you ask one model to hold the context for
Speaker 3: five different disciplines legal coding, creative writing, data analysis, it
Speaker 3: gets mediocre at all of them. It can't specialize.
Speaker 2: So specialization is the fix. Just like with people.
Speaker 3: Specialization is the fix. Just like a real company. You
Speaker 3: don't hire one person to be your lawyer, your coder,
Speaker 3: and your graphic designer. You hire a team of SAR specialists.
Speaker 2: So walk me through how a squad tackles a problem.
Speaker 2: Let's say we're building a new software feature. How does
Speaker 2: that work?
Speaker 3: Okay, so you have a supervisor agent. This is the boss,
Speaker 3: the project manager. It takes your high level request build
Speaker 3: a user authentication page for a new app.
Speaker 2: And it doesn't write a single line of code itself,
Speaker 2: not a line.
Speaker 3: Its only job is to break the goal down and delegate.
Speaker 2: Who does it call? Who's on the team.
Speaker 3: It calls the researcher agent to look up best practices
Speaker 3: for secure logins. It calls the coder agent, which is
Speaker 3: fine tuned on code, to write the actual HTML and CSS.
Speaker 3: But here is the most critical edition. It calls the
Speaker 3: critic agent.
Speaker 2: The critic. I like the sound of that.
Speaker 3: The critic's only job is to look at the code
Speaker 3: the coder wrote and try to break it. To be adversarial.
Speaker 3: It finds bugs, It checks for security flaws, It says
Speaker 3: this code is garbage. It doesn't handle this edge case.
Speaker 2: Do it again. That is fascinating. We are literally building
Speaker 2: the creative tension and quality control of a human team
Speaker 2: into the software itself.
Speaker 3: We are we are replicating the address serial nature of
Speaker 3: human collaboration, and the data shows it works. Google Research
Speaker 3: published a paper just last month in January twenty twenty
Speaker 3: six that highlighted this specific dynamic.
Speaker 2: What was the key finding?
Speaker 3: They found that simply throwing more agents at a problem
Speaker 3: isn't always better. If you have one hundred agents all
Speaker 3: shouting at each other in a flat structure, you just
Speaker 3: get chaos.
Speaker 2: Right, too many cooks.
Speaker 3: Exactly. The key is smart orchestration. It's about the hierarchy.
Speaker 3: A small, well orchestrated team with a clear supervisor and
Speaker 3: critic beats a massive, disorganized mob every single time.
Speaker 2: So the manager agent is actually the most valuable piece
Speaker 2: of code in the stack. Its ability to lead is
Speaker 2: the real intelligence.
Speaker 3: Exactly. The ability to delegate, review, and synthesize is the bottleneck.
Speaker 3: Just like in a human organization.
Speaker 2: Who are the players driving this right now? If I
Speaker 2: want to hire a squad today, where do I go?
Speaker 3: It's the big names you'd expect, but the tools are new.
Speaker 3: Open Ai has their Frontier platform and the Codex desktop app,
Speaker 3: which is heavily focused on this orchestration layer. Anthropic just
Speaker 3: released Claude Opus four point six, which specifically features agent
Speaker 3: teams that you can spin up with basically one click.
Speaker 3: You just define the roles.
Speaker 2: And for the developers listening who want to build their.
Speaker 3: Own, you have open source frameworks like langgraf and crew ai.
Speaker 3: These are libraries that let you build these custom org
Speaker 3: charts in code. You define agent A is the researcher,
Speaker 3: agent B is the writer, and you draw the lines
Speaker 3: for how information flows between them.
Speaker 2: It's like building a circuit board, but the components are
Speaker 2: intelligent workers.
Speaker 3: That's a beautiful way to put it. It's exactly that.
Speaker 2: Okay, we've been very abstract for a while. I want
Speaker 2: to ground this. I want to know what this feels
Speaker 2: like on it Tuesday morning at my desk.
Speaker 3: Let's do it.
Speaker 2: The briefing material had a scenario about a Q two
Speaker 2: sales trip. I want to roleplay this minute by minute,
Speaker 2: because I think anyone who has ever planned a multi
Speaker 2: stop business trip knows the specific kind of administrative hell
Speaker 2: it usually is.
Speaker 3: Let's do it's a perfect use case.
Speaker 2: Okay, set the scene. It's nine zero zero. Am I
Speaker 2: need to plan a trip to Europe to visit twelve
Speaker 2: key accounts in twenty twenty four, I'm opening Expedia, I'm
Speaker 2: opening Outlook, I'm opening Salesforce. I'm checking visa requirements on
Speaker 2: some government website. I'm probably crying a little bit.
Speaker 3: It's a four hour task easily, and you'll make mistakes easily.
Speaker 2: Now, in twenty twenty six, I open my agent interface.
Speaker 2: What do I type?
Speaker 3: You type one single paragraph, something like plan my Q
Speaker 3: two sales trip to Europe for my twelve key accounts,
Speaker 3: book flights and hotels, keep the total budget under eight
Speaker 3: thousand dollars, Schedule meetings with the primary decision makers at
Speaker 3: each company, and prepare customized proposals for each one. Oh,
Speaker 3: and flag any potential risks.
Speaker 2: Okay, a hit enter, does it just spit out a
Speaker 2: PDF instantly? What happens next?
Speaker 3: No, and this is important. It thinks you might see
Speaker 3: a little status bar that says planning workflow or assembling team.
Speaker 3: Then behind the scenes, the squad activates.
Speaker 2: Who goes first, who gets the first assignment.
Speaker 3: The research agent probably kicks off first. It dives into
Speaker 3: your CRM. It's pulling up the contact info for the
Speaker 3: twelve accounts. But it's not just that. It's also going
Speaker 3: to LinkedIn and Google News. It's building a real time dossier.
Speaker 2: What's it looking for?
Speaker 3: Things like client A just had a bad earnings call,
Speaker 3: their budget is tight, they're cost conscious right now. Or
Speaker 3: client BE just hired a new CTO three weeks ago.
Speaker 3: She has a background in cloud security.
Speaker 2: So it's finding the crucial context that I would have
Speaker 2: missed or forgotten to look for.
Speaker 3: Right Meanwhile, concurrently, the travel agent is fighting with the budget.
Speaker 2: Agent, fighting you mean negotiating.
Speaker 3: Literally negotiating. The travel agent finds a great direct flight,
Speaker 3: but it's twelve hundred dollars. The budget agent immediately rejects
Speaker 3: it and says, no, our hardcat for the whole trip
Speaker 3: is eight dollars. That flight is inefficient. Find something cheaper.
Speaker 2: So it's an internal debate.
Speaker 3: Yes, the travel agent iterates, okay, if we fly into
Speaker 3: Munich instead of Frankfurt and take a one hour train.
Speaker 3: We save four hundred dollars. The budget agent.
Speaker 2: Approves, and the calendar agent what's it doing.
Speaker 3: The calendar agent is due the part humans hate most.
Speaker 3: It's already sending out polite, professional emails to the assistance
Speaker 3: of those twelve decision makers. Wow, it says, hi, host
Speaker 3: name will be in town on the fourteenth? Does two
Speaker 3: point zero pm work for a meeting? It's handling all
Speaker 3: the time zone math. It's handling the inevitable back and
Speaker 3: forth of No, he's busy, then how about four point
Speaker 3: zero pm on the fifteenth. It's a full on negotiation.
Speaker 2: And the proposal agent, this.
Speaker 3: Is the heavy lifter. It takes the research from that
Speaker 3: first agent. Client A is cost conscious and it generates
Speaker 3: a slide deck that emphasizes ROI and long term cost savings.
Speaker 3: For Client B, the one with the new CTO. It
Speaker 3: highlights our products advanced security features. It's tailoring the pitch automatically.
Speaker 2: And finally, the risk agent, the paranoid.
Speaker 3: One, the necessary one. It's scanning the horizon. It's checking
Speaker 3: for things you'd never think of. Alert there is a
Speaker 3: planned rail strike in Germany on the fifteenth your travel day.
Speaker 3: Alert the euro To dollar exchange rate is volatile. This
Speaker 3: week budget impact could be five percent higher. Flags these
Speaker 3: things for you.
Speaker 2: So let's say an hour goes by, I get a
Speaker 2: notification on my screen trip planned. I open it up.
Speaker 2: What do I see?
Speaker 3: You see a clean dashboard, You see the full itinerary.
Speaker 3: You see the draft emails ready to be sent to
Speaker 3: your contacts. You see links to the customized slide decks,
Speaker 3: and you see a list of flags for review.
Speaker 2: From the risk agent and at the bottom and at.
Speaker 3: The bottom a single button approve and execute.
Speaker 2: If I hit that button, what happens.
Speaker 3: Gay credit card is charged for the flights and hotels,
Speaker 3: The calendar invites are sent, the files are saved to
Speaker 3: the right folders. It's done. The whole four hour nightmare
Speaker 3: is done in sixty minutes, and you spend maybe five
Speaker 3: of them writing the prompt and reviewing the plan.
Speaker 2: That is seductive. I mean the productivity gain there isn't
Speaker 2: ten percent. It's not even one hundred percent. It's one
Speaker 2: thousand percent.
Speaker 3: That's why companies are scrambling to deploy this. And it's
Speaker 3: not just for sales trips. We're seeing devon style agents
Speaker 3: in software development increasing velocity by three to five x.
Speaker 2: They're writing and testing their own code exactly.
Speaker 3: We're seeing customer service agents resolving a eighty percent of
Speaker 3: tickets and up selling customers to new plans, all without
Speaker 3: a human involved.
Speaker 2: It sounds perfect. It sounds like we've solved work.
Speaker 3: It sounds perfect.
Speaker 2: Uh, let's stop you there, because I don't believe in perfect.
Speaker 2: And that Gartner report, the same one that hyped the
Speaker 2: Year of the Agent, dropped a very sobering statistic that
Speaker 2: you pointed out.
Speaker 3: You're referring to the failure rate.
Speaker 2: Yes, forty percent of agentic projects may be canceled by
Speaker 2: the end of twenty twenty seven. That is almost a
Speaker 2: coin flip. If this tech is so amazing, why is
Speaker 2: nearly half of it destined for the trash heap?
Speaker 3: Because agency brings new, much higher stakes risks. When you
Speaker 3: move from chat to act, the cost of an error
Speaker 3: just explodes.
Speaker 2: Let's talk about that. Hallucinations. We used to laugh about
Speaker 2: them in twenty twenty three when a bot made up
Speaker 2: a court case or a fake historical fact. But what
Speaker 2: happens when an agent hallucinates.
Speaker 3: If a chat bot hallucinates, you read it, you roll
Speaker 3: your eyes, and you say, that's wrong, no harm done.
Speaker 3: If an agent hallucinates, it books a non refundable first
Speaker 3: class flight to Sydney, Australia instead of Sydney Nova Scotia. Oh,
Speaker 3: it transfers ten thousand dollars to the wrong vendor because
Speaker 3: it misread an invoice. It deletes a production database because
Speaker 3: it misunderstood the instruction, clean up the test environment.
Speaker 2: It's a confident mistake with real world often irreversible.
Speaker 3: Consequences exactly, and agents are incredibly confident they will execute
Speaker 3: a bad plan with one hundred percent conviction. And because
Speaker 3: they can run autonomously in the background, you might not
Speaker 3: catch the mistake until the money is gone.
Speaker 2: Which leads to the security nightmare. I've heard this term
Speaker 2: being thrown around shadow agents. It sounds like a spy novel,
Speaker 2: but I'm guessing it's just Bob in Accounting going rogue.
Speaker 3: It is exactly Bob in accounting. Look, these tools are
Speaker 3: becoming incredibly accessible. Bob is tired of copy pasting data
Speaker 3: from Excel to SAP all day. It's boring, so he
Speaker 3: spins up his own custom agent using a simple tool
Speaker 3: like crewe AI. He gives it his log in credentials
Speaker 3: to the corporate network. He tells it. Every day at
Speaker 3: five pm, take this spreadsheet and upload it to this system.
Speaker 3: Do my work for me.
Speaker 2: And now there's an unmonitored, unapproved bot running on the
Speaker 2: corporate network with Bob's permissions.
Speaker 3: A bot that has had no security audit. A bot
Speaker 3: that might be sending sensitive financial data to a public
Speaker 3: LMAPI for processing, violating all sorts of data privacy laws.
Speaker 3: Cybersecurity teams are absolutely terrified. They call it the new
Speaker 3: shadow it. But it's worse because these things act, they
Speaker 3: have agency, so.
Speaker 2: The IT department is scrambling to even find to inventory
Speaker 2: these rogue colleagues they are.
Speaker 3: And then there's the cost. We said operational costs are
Speaker 3: down because you save on labor, but we have to
Speaker 3: talk about cloudshock.
Speaker 2: Cloud shock, what's that?
Speaker 3: Running a react loop is computationally expensive. Every time the
Speaker 3: agent thinks, plans, checks its work, or iterates. That is
Speaker 3: a call to a high end model like GPT five
Speaker 3: or Opus. That costs money. Right now, if you have
Speaker 3: a fleet of agents running loops twenty four to seven
Speaker 3: and one of them gets stuck in a loop, maybe
Speaker 3: it's trying to solve a problem. It just can't solve.
Speaker 3: It can burn through ten twenty thousand dollars of compute
Speaker 3: over a weekend without anyone noticing.
Speaker 2: Ouch. So you come in Monday morning to a bill
Speaker 2: that completely wipes out your quarterly budget.
Speaker 3: It's happening. So this all leads to a massive urgent
Speaker 3: focus on governance. The human in the loop isn't just
Speaker 3: a nice to have feature. It's a non negotiable safety belt.
Speaker 2: You absolutely need that approve and execute button for anything important.
Speaker 2: You need audit logs to see what it did.
Speaker 3: It's the new safety protocol. You wouldn't let an intern
Speaker 3: have the keys to the company bank account on their
Speaker 3: first day, you can't let an agent either.
Speaker 2: So assuming we can put guardrails on Bob's bot and
Speaker 2: we can stop the cloud shock with proper monitoring, where
Speaker 2: is this going. Let's look at the future of work,
Speaker 2: because if agents are doing the research, the booking, the
Speaker 2: coding and the writing, what am I doing? What's left?
Speaker 3: The shift is happening right now. We are moving from
Speaker 3: a world of creators to a world of orchestrators or supervisors.
Speaker 2: Unpack that distinction for me, creator versus orchestrator.
Speaker 3: Over the last thirty years of the knowledge economy, Your
Speaker 3: value was defined by your output. Could you write the code?
Speaker 3: Could you design the slide? Could you draft the legal proof?
Speaker 3: You were the maker, You did the thing right.
Speaker 2: Your hands are on the keyboard exactly.
Speaker 3: Now. The agents are the makers. They create the raw outputs.
Speaker 3: So your value shifts up a level. Your value is
Speaker 3: now can you define the goal with perfect clarity? Can
Speaker 3: you evaluate the plan the agent proposes? Can you supervise
Speaker 3: the team of agents in spot when they're going off track?
Speaker 2: So the new essential skill is leadership, But it's about
Speaker 2: leading robots, not people.
Speaker 3: And that is a different skill set. It requires extreme
Speaker 3: clarity and precision. Ambiguity is the enemy of an agent.
Speaker 3: If you give a vague instruction to a human, they'll
Speaker 3: probably ask for clarification or use their common sense. If
Speaker 3: you give a vague instruction to an agent, it will
Speaker 3: find the most literal and often most disastrous way to
Speaker 3: interpret it.
Speaker 2: So we all have to become prompt engineering project managers.
Speaker 2: That's the new job in a way.
Speaker 3: Yet, yes, the question changes from will agents change everything?
Speaker 3: To how fast can you learn to lead them effectively?
Speaker 2: And looking a bit further out, let's say twenty twenty
Speaker 2: seven twenty twenty eight. If this trajectory holds, what does
Speaker 2: the economy even look like?
Speaker 3: We expect to see fully autonomous ecosystems. We're talking about
Speaker 3: ninety percent of B to B buying being intermediated by agents.
Speaker 2: Ninety percent. That seems incredibly high.
Speaker 3: Think about it. Why would a human procurement officer want
Speaker 3: to have six phone calls with a human sales rep.
Speaker 3: It's slow, it involves small talk, it involves human bias
Speaker 3: and scheduling conflicts.
Speaker 2: True, it's inefficient.
Speaker 3: In the very near future, my company's buying agent will
Speaker 3: talk directly to your company's selling agent. They will negotiate
Speaker 3: pricing based on real time supply and demand data. They
Speaker 3: will verify compliance with regulations. They will draft and sign
Speaker 3: a smart contract. They will execute the transfer of money.
Speaker 2: So we're talking about trillions of dollars in B to
Speaker 2: B spend.
Speaker 3: Happening conversationally between algorithms. We're talking about fifteen trillion dollars
Speaker 3: in spend being handled with human means only supervising the process,
Speaker 3: not executing it.
Speaker 2: The global economy becomes a conversation between.
Speaker 3: Machines while we sleep or while we do more interesting things.
Speaker 2: Hopefully that is a lot to process. It's exciting, but
Speaker 2: it's also a little alienating.
Speaker 3: It is it's a new layer of abstraction over reality,
Speaker 3: and that always feels strange at first.
Speaker 2: So let's wrap this up. If we have to summarize
Speaker 2: the year of the Agent, it's that we have crossed
Speaker 2: the rubicon. We aren't going back to dumb chatbots. This
Speaker 2: is the new reality.
Speaker 3: No agents that can plan, act and adapt. That is
Speaker 3: the new baseline for what we expect from AI.
Speaker 2: But the big picture takeaway from ME isn't just about replacement.
Speaker 2: It's about the friction of adoption. It's about the fact
Speaker 2: that these things are brilliant, but they are also dangerous
Speaker 2: if unmanaged. They are powerful new employees that need to
Speaker 2: be trained and supervised.
Speaker 3: It's about partnership, but it's a partnership where you have
Speaker 3: to be the senior partner. You have to be the
Speaker 3: vigilant boss, at least for now.
Speaker 2: I want to leave our listeners with a thought that
Speaker 2: struck me while I was reading through the source material
Speaker 2: on the human and the loop concept. It's a bit
Speaker 2: of a provocation. Go for it. We talk about how
Speaker 2: great it is to have these digital colleagues working twenty
Speaker 2: four to seven. They don't sleep, they don't take breaks,
Speaker 2: they learn our preferences perfectly. They handle our money. But
Speaker 2: here's the catch.
Speaker 3: Go on.
Speaker 2: If the agent is doing the work at light speed,
Speaker 2: analyzing data and generating plans in seconds, and then it
Speaker 2: has to pause and wait for you to log on,
Speaker 2: read the report and hit approve, at what point do
Speaker 2: you stop being their boss and start becoming their bottleneck.
Speaker 3: That is the ultimate question, isn't it.
Speaker 2: If your team works twenty four to seven at the
Speaker 2: speed of light and you work nine to five at
Speaker 2: the speed of a human, you are the slow link
Speaker 2: in the chain. You are the friction.
Speaker 3: It forces you to up your game or to trust
Speaker 3: the system and step aside.
Speaker 2: Exactly So, tomorrow morning, when you look at your to
Speaker 2: do list, I want you to ask yourself which part
Speaker 2: of this requires my soul, my creativity, my judgment, and
Speaker 2: which part belongs to the agent Because the agent is
Speaker 2: ready for the.
Speaker 3: Handoff and it's just waiting for your instruction.
Speaker 2: Thanks for listening. We'll see you next time.