The Evolution of AI Agents: Lessons from 2024, with MultiOn CEO Div Garg
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What is the current state of AI agents and how has it changed since 2023?
I think OpenI has definitely l lost a lot of the lead they used to have. But GP D four where like they were kind of the sole winner and no one could catch up with them. At at this point it does seem like a very homogeneous market where everyone is kinda close. When anything's disruptive, I think that it takes a lot of time for that to catch on. And I think we're we are at the start of the disruptive era where like a lot of the online communication and interaction will g get disrupted by g humans are He's pretty good at like navigating websites with UI. And so like theoretically, like uh yeah, I can also become very, very good. Even better. I would call it the explosion of applications. Which I I don't think has happened to five if you think about agent d applications.
I think it's still legal.
Hello and welcome back to the Cognitive Revolution. Today, Div Gerg, founder and CEO of Multion, returns for his third appearance on the show. A lot has changed in the AI agent landscape since I first spoke with Div in mid twenty twenty three. As you might remember, at that time, the AI community was a buzz about the potential for AI agents. With projects like Baby AGI giving large language models access to tools, and really only very minimal guidance, and then stepping back to watch and see what they could accomplish on their own. Of course, as it turned out, they didn't accomplish all that much. While there were many amazing moments, those were the outliers. On average, a step-by-step error rate, including on very mundane micro tasks, was too high for agent frameworks to successfully string many step sequences together all that often.
And we also learned that while large language models can improve in many areas through self critique, they have a tendency to get stuck on obstacles that humans quickly find ways around. For that reason, much of the last eighteen months of work on agents has gone into developing better and more prescriptive scaffolding, with many companies ultimately delivering platforms for what I call intelligent workflows. That is, workflows that a human has designed, and where the AI is needed to do some important subtask which requires intelligence, but where the AI is not given freedom to choose its own adventure. As of January this year, Div and the Multion team were still among the most bullish on open ended agents, and as you'll hear in this conversation, they have continued at least partially to buck that trend.
They have built some new scaffolding and they have developed interesting techniques for domain specific fine tuning, but their agent continues to take arbitrary natural language requests and gamely does its best to fulfill them. The progress I found in my testing is pretty obvious, and in some contexts the company claims human level performance. But still, the system as a whole is not a viable substitute for a human assistant. With that in mind, I was excited to pepper Div with questions about what he's learned from all of this activity. And so in this conversation we unpack the latest in agent development, including the company's data collection strategy, the seemingly missing market for human computer use data, and the role of synthetic data in bridging that gap.
The company's model strategy, including what models they've chosen as base, what fine tuning techniques they're using, and how their computer vision approaches have evolved over time. Why benchmarks so often show human level performance, while the real world results are clearly not as strong? The future of agent authentication, as well as which parts of the internet at large will compete to serve agents versus which parts will try to exclude them. And finally, what sorts of customers Multion is looking to partner with now, as well as how they're thinking about competing with hyperscalers in light of Claude's new computer use capability. Overall, it's clear to me that while it's taken longer than I had expected, reliable agents that can perform a very large percentage of routine computer use tasks are coming.
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Chapters
8 chapters
1
What is the current state of AI agents and how has it changed since 2023?
0:00–12:30
2
How did the shift from open‑ended to on‑rails agent architectures affect performance?
12:30–25:20
3
What data‑collection strategies does MultiOn use to train reliable agents?
25:20–35:32
4
How does MultiOn fine‑tune its models and why does it favor LoRA over full‑parameter training?
35:32–46:41
5
What is Agent Q and how does it improve success rates on web tasks?
46:41–56:56
6
Why do benchmark results often look better than real‑world agent performance?
56:56–1:09:18
7
What are the biggest challenges around agent authentication and website security?
1:09:18–1:19:43
8
What does the future of AI‑assisted everyday life look like in the next year?
1:19:43–1:24:01
Speakers
2 identifiedMore from "The Cognitive Revolution"
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