Podcast | Agentic AI Unleashed: Orchestrating the Future of Work

Agentic AI Unleashed Orchestrating the Future of Work

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Transcript:

Welcome to the deep dive. Today we're really cutting through the noise to look at the agentic AI overhaul. It's, uh it's a pretty fundamental shift.

Absolutely.

And maybe the best way in is this wild story from the freight industry. Picture this. A logistics pro gets a call from an AI.

Okay.

And it sounds completely human, like natural voice, pauses. It even had background noise dialed in.

Wow.

The only reason they knew it was AI. It was programmed to say so. Cause, well, instant panic in the sector.

And that story, it's not just color. It's crucial. It tells you first, the tech is incredibly convincing now,

Right? Almost too convincing.

But second, it points straight at the why behind this overhaul, the AI scaling challenge. You know, CEOs, their confidence in AI for growth, it actually dropped 15%.

15%. Why?

Because only about 18% of companies, I think that's the figure, have actually managed to get their different AI tools talking to each other. They bought the pieces but couldn't build the system.

Okay. Okay. So, isolated tools are the problem and agentic AI that's the solution because it's about what's the word you used orchestration.

Exactly. Orchestration. We're moving past simple single responses, think, coordinating a whole system of intelligence agents working together on complex jobs.

But hold on, if orchestration is the goal, why didn't those huge single super agent models work out? Shouldn't one big brain handle everything.

Yeah, it seems logical, right, but in the real world, especially in operations, uh, specialized agents win.

Why is that?

Well, the super agents, they tend to have higher latency. They're slower.

Yeah.

And their decision quality, it drops for those high-stakes, repetitive tasks, things like, you know, logistics operations.

So, what we're seeing is this rise of specialization, agents designed for what you might call the tactical layer.

The tactical layer, like the day-to-day stuff.

Exactly. The high volume grunt work. Think about companies like Lunapass and Logistics.

Right?

They build specific agents for things like track and trace. POD Chase, you know, getting that proof of delivery.

They're chasing the paperwork. Got it.

Yeah. And these focused agents, they handle thousands of calls, documents, updates. And the result, we're talking like a 45% cut in labor costs. Yeah. For those specific tasks.

45%. That's that's huge. So, it follows that specialist agent framework idea. Like, uh, an outbound voice agent just focuses on getting status updates 95% of the time.

Precisely. It calls, it texts, it records the updates straight into the company's TMS, their management system. That focus just beats a general model trying to juggle 10 things.

Okay, that specialization makes sense. But how do you build and manage this whole orchestra of specialists? There must be a blueprint.

There is. And the sources point to something called the CORE framework. C O R E. Context, outcome, reasoning, and evolution.

CORE. Okay, break that down. What's the key part?

The heart of it is the reasoning engine. That's the brain basically. But it needs that feedback loop to learn and get better. That's the evolution part. So how do the other parts fit in? CORE, right?

Context gives the agent the data it needs right now.

Yeah.

Outcome defines the specific goal hit that 95% status update target. Reasoning figures out the steps to get there and evolution makes sure it learns. You really need all four working together.

Okay. So, you use CORE, you build this finely tuned machine. What happens to the people? Are we just talking about replacing jobs?

No, not entirely. I mean, some tasks get automated. Yes. But the bigger shift is about redesigning work itself. Building human agent teams,

Redesigning work. How?

Well, the sources talk about four models. Yes, there's full automation, but there's also augmentation where the AI makes the human worker better, faster.

Okay.

Then you have agent-led processes where the AI does the bulk, but a human oversees it. And critically, there are still fully human-owned tasks, the really high-stakes decisions needing judgment.

So, it's a mix, which must mean new kinds of roles are emerging.

Definitely, it's creating basically three new digital personas in the workforce. Yeah. The vast majority, maybe 80% are citizens. They just use AI tools daily in their regular jobs.

Makes sense. Like using Copilot or something.

Exactly. Then you have the translators. That's maybe 15%. They're crucial.

Yeah.

They bridge the gap between the business need and the tech side. They define the outcomes for the agents.

Okay. The bridge role and the last group,

The wizards, maybe 5%. These are the folks actually building and maintaining these complex orchestrated systems. The deep tech experts,

Citizens, translators, wizards. Okay, so pulling it all together from our sources, the real takeaway here is moving beyond just collecting tools.

Absolutely.

It's about strategic orchestration. That seems to be the dividing line.

It really is. And maybe the final thought for everyone listening is this.

Take a hard look. Are you just, you know, collecting more and more AI tools that work in isolation, or are you actually stepping back, thinking strategically, and starting to orchestrate that intelligence across your whole operation? Because making that shift, that's what's going to define the winners in this next phase.