AI Agents Are Not LLMs / Chatbots

by
Rise Ooi

Revealed: July 11, 2025 at 10:24 am Up to date: July 11, 2025 at 10:28 am

by Ana

Edited and fact-checked:
July 11, 2025 at 10:24 am

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In a single yr, the world will bear in mind chatbots the way in which it remembers fax machines: a clumsy step on the highway to one thing higher. Ask any COO about their chatbot rollout, and you will note the identical well mannered shrug: “It’s clunky, it’s excessive upkeep, it fails at answering FAQs. We nonetheless want people.”
We’ve all been there. You attempt to alter the supply time or deal with for an essential parcel. A chatbot politely replies that it has taken word of your request and can now get a human buyer assist personnel to execute the logistics of it. It doesn’t take every other motion past that. You’re feeling pissed off.
Right here’s the truth: the chatbot period is over. Enterprises that cling to it would bleed time, cash, and expertise. A brand new breed — autonomous AI brokers — is stepping in, and the gulf between the 2 approaches will determine which corporations dash forward and which keep trapped in customer-service purgatory.

How We Acquired Caught with Zombie Chatbots
Early chatbots have been alleged to be the frontline of automation. As an alternative, they turned everybody’s least favourite buyer expertise. Why? As a result of they have been by no means constructed to grasp something.
They have been rule-based from the beginning. Hardcoded scripts, linear resolution bushes, “if this, then that” flows that explode in complexity rapidly. Say the precise proper phrase they usually reply. Deviate even barely, and also you’re both ignored or looped again to the start. Like an IVR menu with higher manners. The exponential branches are what make conventional chatbots unimaginable to take care of past 20 widespread use circumstances, not to mention ship ROI.
And the issue isn’t simply unhealthy UX — it’s architectural. Guidelines-based methods don’t generalize. They will solely reply to predefined inputs and eventualities. The second one thing adjustments — a coverage replace, new pricing tier, a buyer asking a sound query barely otherwise — your complete move collapses.
What occurs subsequent? Escalation to people. Time and again.
In the meantime, frontline employees are caught doing the identical repetitive duties the bot couldn’t end — manually updating delivery information, calling the driving force, logging the replace — whereas the dashboard experiences a “profitable interplay.” Who’s it actually working for?
Right now, most enterprise “AI chatbot” deployments are little greater than glorified resolution bushes. Beauty enhancements — friendlier tone, branded avatars — can’t change the underlying actuality: they’re brittle, shallow, and get caught simply.
However these bots have been bought as silver bullets. So corporations saved investing, hoping every new launch would lastly shut the loop. It didn’t. It couldn’t. As a result of the structure was by no means constructed for autonomous understanding or motion — it was constructed to deflect tickets.
That’s why most chatbot KPIs are surface-level: CSAT, handoff price, session size. The second you ask, “Did it truly clear up the issue?” the dashboards go quiet.
If you rejoice chatbot metrics, you might be mainly celebrating a treadmill for distance travelled. Merely put: numerous movement, nowhere to go.

Then Got here the LLMs — Talkers, Not Doers
Enter GPT and its cousins. All of the sudden, bots might maintain conversations. They understood slang. They dealt with ambiguity. They remembered issues and have a protracted context reminiscence.
It felt like magic. And it was a real leap ahead. For the primary time, AI might generate human-like responses at scale. AI is clever.
However right here’s the catch: LLMs are good improvisers, not operators.
They don’t have structured targets. They don’t “know” when a job is full. They will’t reliably entry, replace, or implement enterprise guidelines with out scaffolding. What they produce is language — compelling, articulate, and infrequently helpful, however hardly ever accountable.
When an LLM tells you it has submitted your request, it hasn’t. Until it’s wrapped in an orchestration layer that bridges language to motion, it’s nonetheless simply speak.
So whereas LLMs moved the trade ahead, they didn’t clear up the execution hole. They created a brand new class of false expectations. Now, customers aren’t simply pissed off with bots — they’re confused by AI that sounds good however can’t truly assist.
That confusion is what leads us right here: to AI workflows and AI brokers.

What an AI Agent Actually Is
An AI workflow is an LLM that executes instructions with predetermined steps. However usually in the actual world, steps can’t be predicted beforehand.
That’s the place AI brokers are available. It’s an LLM that integrates with exterior instruments, in a position to cause deeply, and — utilizing the whole lot it has entry to — solves complicated issues that may take people orders of magnitude longer to do.
AI brokers obtain this by combining all three layers.
First, a dialog layer that’s usually an LLM to interpret intent (sure, LLMs are helpful, it’s simply that calling an LLM an “AI answer” by default is like calling dial-up modems WiFi); second, a reasoning layer that outlines all the foundations, insurance policies, and job planning that determine what ought to occur; and third, an execution layer with safe connectors into CRMs, ERPs, fee rails, voice methods, and no matter legacy monster hides within the closet.
Take away any layer and the tower collapses. Preserve them collectively and the system strikes from “reply” to “resolve.”
Let’s revisit the state of affairs of the shopper who must reroute a parcel.
Historically, chatbots can full step one — ticket dealing with. LLMs may get you one step additional. Then a human must step in. They make selections, then sort replies manually. That is painful. Now an AI agent proactively executes complete workflows, makes autonomous selections, interacts with backend methods, and logs actions for audit functions, all with out human intervention except completely crucial.

Picture credit score: Jurin AI

The agent does in thirty seconds what would in any other case ping-pong throughout a number of departments. It owns the duty, from begin to end.

So Let’s Cease Calling Every part an “Agent”

The time period “AI agent” is having its second — however like all good buzzwords, it’s being stretched skinny. Each vendor with a chatbot and an API now claims to supply “brokers.” Some even use the phrase simply because their LLM remembers your title for 5 turns.

This misuse isn’t simply branding fluff — it causes actual confusion. It trains patrons to anticipate outcomes from instruments that have been by no means designed to ship them. It slows down adoption by creating false expectations, adopted by actual disappointment. Worst of all, it lets enterprises persuade themselves they’re innovating, when all they’ve executed is bolt a brand new UI onto the identical outdated service desk.

However the AI transformation is actual.
True AI brokers aren’t simply extra conversational. They’re extra accountable. They combine deeply, act responsibly, and ship traceable, business-critical outcomes. They aren’t simply an interface — they’re infrastructure.

And we’re solely at the start.

The Way forward for Data: From Apps to AI Brokers
For years, we’ve tailored to the logic of machines. We’ve clicked by menus, memorized interfaces, juggled 5 tabs simply to finish a job. Search bought smarter, apps bought sleeker — however the burden stayed on the consumer.

AI brokers flip that.

As an alternative of asking you to learn the way the system works, the system learns how you’re employed — by pure dialog.

Need to e book your journey? Simply chat along with your non-public AI concierge:
“Plan a climbing journey within the Alps, early September, off the crushed path.”
And it occurs. Flights, motels, native guides — even hidden gems you’ll by no means have found by yourself. No 90s web sites or clunky cellular apps with unhealthy UX. Only a dialog that will get issues executed.

It is a shift from apps you use to brokers that function in your behalf.

And it gained’t cease at journey. Brokers will reshape how we work together with the whole lot — logistics, procurement, compliance, HR. Quietly reworking brittle instruments and fragmented workflows with clever methods that may cause, act, and enhance over time.

That is the agentic future: the place duties are accomplished immediately by way of voice or textual content by AI that understands, acts, and delivers — your very personal govt assistant.

It’s not a sci-fi imaginative and prescient. It’s only one to 2 years away. And we’re already constructing towards it at Jurin AI.

The age of agentic AI is right here, and we’ve solely scratched the floor. I’ve by no means been extra excited.

About The Writer

Rise Ooi is a three-time tech founder, engineer, and investor recognized for figuring out billion-dollar alternatives early. He helped scale Utilized Instinct right into a multi-billion-dollar unicorn by constructing its Asian presence from the bottom up and now leads Jurin AI, the place he’s assembling a world-class crew to reshape office productiveness throughout Asia-Pacific. A former AI scientist at Japan’s nationwide labs, Rise brings deep technical and international experience to the whole lot he builds.

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