Darren Patterson

I think it's like, it's very close.

Just a, just a few trillion dollars, right?

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.

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.