Agentic AI: From Chatbots to Digital Teammates

Lachlan Reed

Welcome Everyone to this latest episode of the human workforce podcast series. I am Lachlan Reed leading us off today with my co-host Simon Carver. let’s get into today’s topic which is pretty simple to say and a bit slippery to explain: agentic AI. In plain English, it’s AI moving from just answering questions to actually taking action. Not just, “Here’s a summary, mate,” but, “I’ve started the process, checked the details, passed it to the right system, and lined up the next step.” That’s the big shift we’re unpacking.

Simon Carver

Yeah, and that’s why it fits the whole Human Workforce theme so well. This isn’t just a story about better software. It’s really a story about work changing shape. The question stops being, “Can AI write something for me?” and becomes, “What parts of a job can be carried from start to finish by a digital teammate, while a human still owns the outcome?”

Lachlan Reed

Exactly. We’re out of the old chatbot phase, or at least moving past it. A chatbot is like the bloke at the servo who gives you directions. Helpful, sure. Agentic AI is more like the mate who says, “Hop in, I’ll drive, grab the parts, ring ahead, and sort the paperwork.” That’s a terrible analogy. Actually no, I’m standing by it.

Simon Carver

No, it works. Because the key thing is sequence. One agent can kick off a task, another can verify information, another can assess risk or priority, and another can follow up. So instead of one tool giving one response, you get a chain of coordinated steps. Intake, verification, routing, escalation, follow-up. That’s real workflow.

Lachlan Reed

And those workflows can get pretty chunky. Say a customer asks about financing, support, insurance, booking, whatever industry you’re in. Old-school AI might explain the policy. Agentic AI can gather documents, pull in outside data if it’s allowed to, check if the details line up, send it to the right reviewer, and nudge the person if something’s missing. It’s not magic, but crikey, it does cut out a mountain of back-and-forth.

Simon Carver

That’s the practical leap. And I think people miss this because “agentic” sounds futuristic, almost theatrical. But the use case is surprisingly ordinary. It’s the work nobody brags about at dinner: sorting spreadsheets, checking records, moving requests between systems, preparing files, watching for exceptions. The boring middle of work, basically.

Lachlan Reed

Yep. The glue work. The invisible stuff holding the whole shop together. In heaps of companies, that glue has been done by humans copying one field into another, checking if names match, chasing approvals, updating status notes. If AI agents can do that ten times faster and more consistently, then the whole job gets redesigned.

Simon Carver

And that’s important: redesigned, not just accelerated. Because when a system starts handling multi-step work, companies don’t just save time. They reorganize roles. One person might oversee a whole cluster of automated processes instead of manually touching every case. Growth starts to separate from headcount a bit. More output doesn’t automatically mean more hiring for the same repetitive tasks.

Lachlan Reed

Which can sound a bit spicy, honestly. People hear that and think, “Right, so the robots have nicked my job.” But the more accurate version from the material we looked at is that the work moves. From doing every tiny step by hand to supervising, checking, stepping in on the hairy cases, and making sure the thing’s not going off like a dodgy trail bike carburetor. Ask me how I know.

Simon Carver

And that’s where the human piece gets more valuable, not less. If the machine can process, sort, and draft, then the person becomes the one who decides when something feels off, when a client needs empathy, when an exception matters, when a rule collides with reality. Agentic AI isn’t just a banking trend or a tech trend. It’s a model for redesigning everyday work across the board.

Simon Carver

So let’s widen the lens. The reason this matters across every industry is that the same pain points keep showing up everywhere. Operations teams drown in repetitive handoffs. Compliance teams chase documentation. Customer service people answer the same questions over and over. Sales teams update records instead of building relationships. Risk teams sift through alerts, many of which go nowhere. Different logos, same headache.

Lachlan Reed

Yeah, whether you’re a giant firm or a tiny outfit with six people and one stressed office manager, the pattern’s familiar. Too many systems, too many checks, too many copy-paste jobs. And usually too many alerts that turn out to be nothing. In the source material, one example was those old alert systems where most flags still needed a human review even when they were basically false alarms. That’s a massive time sink.

Simon Carver

Right. So agentic systems come in and do triage. They pull from structured and unstructured information, compare records, look for context, and only escalate the knotty cases. It’s like having an assistant who doesn’t just stack every envelope on your desk, but opens them, sorts them, and says, “These three matter now, these ten can wait, and this one is weird, please look at it yourself.”

Lachlan Reed

That same logic applies to cyber defense, fraud checks, loan reviews, claims processing, onboarding, supplier approvals—you name it. Anywhere there’s a queue, a checklist, and a bit of risk, agents can probably take a swing at the repetitive bits. They can scan for patterns, update rules fast, even isolate a problem quickly if the setup allows it. Fighting fire with fire, sort of.

Simon Carver

But—and this is the whole human-centered point—they shouldn’t own judgment in a vacuum. The shift is from doer to supervisor. Humans stop being the hands for every small action and become the overseers of the workflow. They monitor quality, resolve exceptions, explain decisions, and keep accountability where it belongs.

Lachlan Reed

That’s the bit I reckon people need to hear twice. You may not need to be the person typing every field into every form. But you absolutely need people who can manage twenty digital workers at once, understand what data fed the result, spot bias or hallucinations, and say, “Hang on, that recommendation doesn’t pass the pub test.” Maybe not a regulatory term, but it should be.

Simon Carver

Honestly, I’d support that. The pub test for AI. But yes, explainability matters. If a system makes a recommendation about risk, eligibility, or prioritization, somebody has to be able to show the reasoning trail. Not just because leaders want confidence. Because regulators, clients, and internal teams will ask, “Why did this happen?” Black-box answers won’t cut it.

Lachlan Reed

And the more these systems move from suggesting to acting, the more you need guardrails. Human-in-the-loop for high-risk stuff. Thresholds. Permission checkpoints. Clear dashboards. Audit trails. Strong data boundaries. The source also pointed to more private, fortified setups for sensitive workloads, which makes sense. If you’re gonna run a digital labor force, you don’t leave the shed door wide open.

Simon Carver

Then there’s upskilling, which I think is the hopeful part. People don’t need to become coders overnight. But they do need data literacy, enough to understand where information comes from and whether it’s trustworthy. They need orchestration skills, meaning how to instruct and monitor AI systems. And they need stronger soft skills, because the more machines handle drudgery, the more human work centers on trust, clarity, and relationships.

Lachlan Reed

So maybe the headline is: don’t fear the AI, become the person who can steer it without losing the human plot. That’s where the value is heading. We’re gonna keep digging into this in future episodes, because there’s a lot in it. But for now, that’s the first pass. Simon, good yarn.

Simon Carver

Good yarn, Lachlan. And thanks for listening, everyone. We’ll keep puzzling through this messy, fascinating shift with you. See you next time.

Lachlan Reed

Catch you next one. Bye.