Chapter 1: What is the main topic discussed in this episode?
I met a customer in New York City a couple of weeks ago. He says, my boss tells me he needs agents and I just keep building make workflow automations and I tell them there are agents and he's very happy. So by empowering that AI agent that has a role and understanding what it should do and then giving it the right tools and very precisely focused on giving it success.
scalpels, not sledgehammers. We learned more about the capabilities of generative AI. We learned AI can actually reason, and that's an interesting thought unto itself. And if AI can reason, it means it's not just part of the workflow, but it's actually driving the workflow. And ironically, the more you throw at it, the less successful it is.
They have people who, for whatever reasons in the past, have shown themselves not capable of successfully taking care of a pet. So the do not adopt list, it's name a person, maybe an address, phone number. The business process is making sure that a person that comes in is not on that do not adopt list. Fuzzy matching is like a hard software problem to solve normally.
Like, is this is Tom Thomas and Thomas Tom? Like, are these the same person? It's a baseline expectation when you come to work now.
Chapter 2: How do AI agents differ from traditional automation workflows?
So imagine building hundreds of automation workflows across your company, sales, marketing, customer service, and you're scared to change anything because you don't know what you might break. That's the reality for a lot of companies today with AI and automation. And today we're going to talk to Make about how to solve this exact problem.
Welcome, humans, to the latest episode of The Neuron Podcast. I'm Corey Knowles, editor of The Neuron, joined as always by our daily writer, Grant Harvey. How are you, Grant?
Doing good, Corey. Doing great, actually. Really excited about this one.
Nice, nice. Well, today we're going to talk with Darren Patterson, VP of Market Strategy at Make. the automation platform used by about 250,000 organizations worldwide. Mick just launched two awesome new products, AI agents that can make autonomous decisions in your workflows and make Grid the world's first real-time visual map of your entire automation landscape.
And Darren has a fascinating take that goes against what everyone's saying right now. While the industry is screaming AI agents everywhere, he's arguing that companies need to get their automation house in order first, or they're setting themselves up for failure.
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Chapter 3: What role does automation play in business processes?
Darren, welcome to The Neuron. Great to have you.
Corey and Grant, it's great to be with you. I'm actually a big fan of your newsletter and read it every day before I get started. Oh, that's awesome. Thank you. I really appreciate that.
So, Darren, I guess our first question is something Corey and I talked a bit about is we usually tell people that there's a lot of companies that are, you know, marketing AI agents, let's say, but they're actually just, you know, agentic or automation workflows. Both are cool. But for an AI to be an agent, it needs to reason and act on a user's behalf. So which one is make building?
All of the above. Actually, I describe it as either we were prescient or quite lucky, but we've been doing automation for some 10 years now. And of course, the world of automation has changed significantly in the last few years, as you guys would certainly know. And that fateful day in November, I think it was 2022, when ChatGPT became known to the masses,
Chapter 4: How can businesses avoid automation sprawl?
That was a moment in time that we saw people begin to experiment with, how do I actually incorporate AI into my business processes? And so Make was actually supremely suited to be able to take advantage of that trend and enable you to experiment very quickly with incorporating it into your business process.
But of course, as we learn more about the capabilities of generative AI, we learned AI can actually reason. And that's an interesting thought unto itself. And if AI can reason, it means it's not just part of the workflow, but it's actually driving the workflow. So we actually commonly describe automation among a spectrum.
And the spectrum has, on one end, automated workflows that are highly deterministic. And on the other end, you have agentic workflows that are highly non-deterministic, and you're giving a lot of flexibility to an AI agent to make decisions. Of course, in the middle, there is even workflows that have steps that are AI to generate content or analyze content or whatever the case may be.
We have a firm belief that all those approaches are absolutely valid for businesses, both small and large, to be able to really achieve significant value from the automation spectrum. And of course, agentic parts of it, but non-agentic parts of it as well.
I agree. Yeah. I think there's use cases for both, like everywhere.
Yeah.
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Chapter 5: What is the significance of Model Context Protocol (MCP)?
Increasingly, we see people, they need a skill set to figure out when to apply the right one, right? So it's not a black and white sort of piece. So understanding what's important for that particular business capability.
It is. And man, you mentioned it, the automation industry was just... so perfectly placed and teed up for all of that to happen. It's like, it's, it's, it's almost as interesting as, you know, Nvidia and chips. It's this whole idea that, that like, here's this tool. It's already there.
You know, we're almost on the scale of Nvidia. I think it's like, it's very close. Just a, just a few trillion dollars, right?
Yeah.
You'll get there. You'll get there.
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Chapter 6: How can businesses scale AI safely within their operations?
So I guess what does a make agent do that standard automation workflows can't? And when does the difference actually matter for a business?
Yeah. So agents operate most effectively environments where rules are hard to define or they may change over time. And so a make agent deployed into a business process works similarly to some other agents that people might have interacted with in the sense that I give this agent a high-level role in life, so to speak. It has a goal.
It has a set of instructions and kind of criteria that I want it to operate with. And then, of course, what makes an agent an agent, it's ability to make decisions about how to carry out that goal.
So Make is, again, ideally situated because, of course, if I want my agent to be able to effectively do things for me or access information for me in order to make the right decisions on that overall goal, I needed to connect to my tech stack. So, of course, that agent's empowered with lots of tools. Make itself has over 3,000 built-in applications or connectors to make
all the different types of applications that run a modern day business, everything from your Airtables and monday.coms to your NetSuite implementation or your Oracle implementation. So by empowering that AI agent that has a role and understanding what it should do, and then giving it the right tools and very precisely focused on giving it scalpels, not sledgehammers.
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Chapter 7: What are some real-world examples of using AI agents?
We'll talk a little bit about that if we get a chance. Then I can make sure that the agent can identify what tools to use when and let it go on its way. And what's most important about using an agent versus a workflow automation is if those rules change over time, if I have, for example,
refund policies that determine what customers I'm going to refund in an online e-commerce store, then those rules are nuanced. They change all the time and agents respond really well to that as opposed to having to redesign a very complex workflow and think through all the potential edge cases. And so that's where agents really shine. They also shine in terms of their resilience, so to speak.
Unfortunately, in the world we live in, even now, especially maybe now with a company that rhymes with proud flair, it goes down from every once in a while. The systems go down. And so the system goes down and in traditional automation, you know, it stops. But an agent says, well, what's going on?
And it retries and it thinks through edge cases and it can think through and reason, so to speak, in order to make things happen effectively. So this is kind of where we see the differences mostly occur between that kind of traditional automation approach and where an agent might make sense.
That's awesome. How do you, what's the trick to really recognizing that difference as far as like, is what I need just an automation here?
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Chapter 8: How can organizations prepare for future automation challenges?
Is there, do I need to go with an agent? Should this be a human?
Yeah. I don't know if it should be a human. I subscribe to the world where we're all going to end up on the beach enjoying life and what that looks like. We'll circle back on that. All right. We're all agreed. Perfect. I mean, certainly there are still times that human intervention is required or desired based upon the type of business process that you're interacting with. But actually...
I really appreciated OpenAI actually produced a fantastic paper on exactly what are agents good at versus what are they not good at.
And it's really looking at the types of automations then that are resistant to being able to be automated because of the fact that they're highly dependent upon context that changes all the time or different qualitative types of inputs that would be struggled with in a normal automation environment. They have a fantastic paper.
I actually refer to it all the time on the five reasons you would want to use agents in your particular process. I lean on that a lot.
We'll drop a link to that in the description below this video too. Beautiful. That's excellent.
I think what's interesting is to make sure that people don't just start with the solution and they start with the problem they're trying to solve. All too often today, especially people are like, I need an agent for this and I need an agent for this and I need an agent for this. I met a customer in New York City a couple of weeks ago.
He says, my boss tells me he needs agents and I just keep building make workflow automations and I tell them these are agents and he's very happy.
He's very happy.
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