AI Agents vs Workflows: How to Choose (Animated)

Today we're going to be going over automations and AI agents. By the end of this video, you will know enough to go forth and master and use these tools in a way that's cheap, reliable, and gets you the results you want. Let's start with what an automation is. It's some sequence of events that runs without human intervention. One way to think about it is as a bunch of if then statements. So, if the email is from Joe, then label the email as finance. In many products, including Carly, these automations are called workflows. One property of a workflow is that it is typically deterministic. If something's deterministic, it means that if you run the same thing twice, there is only one possible result. If it's from Joe, then it gets the label finance. If I run it again, nothing changes. It gets the label finance. This means that as long as the decision rules you write are good, you will be able to guarantee an outcome. Workflows are also incredibly cheap to run. This is because they don't involve LLMs, large language models like ChatGPT or Claude. But with deterministic workflows, there are no large language models, no AI. This means that the cost is basically free for the user to run. The biggest con is the setup time. To have a really useful workflow. There are typically a number of steps and a lot of them you won't even think of. Let's say that on September 16th, Joe sends me an email. It says hello. Well, what happens if it's within this time frame, which it is, my system will respond, Sarah is out of office. When it comes to email, there are ways of spoofing emails. Meaning Joe can write me an email that looks like it's coming from the president of the United States. So, what if Joe takes a random email, maybe it's that of the president of the US, and writes an email to me with some scammy thing in it, and suddenly my email is telling a thousand different people that they've won a free cruise, and I'm unwittingly involved in this scam. What this is meant to show is that our simple workflow here is really incomplete, and there's always more than meets the eye when it comes to workflows. So, now let's move on to AI agents. The simplest way to get your head around AI agents is it's a set of prompts which are instructions and then tools which are things the agent can do. So here's an example of an agent prompt. You're a sales agent. You work for this company and then you would include the company name and probably a bunch of information on it. Your goal is to qualify the lead and send a Stripe link when they ask to get started. Now, what tools would an agent like this need? Well, we need Stripe. Stripe is a payment processor because it would need to be able to generate a link that would allow a person to check out. Let's talk about the pros of using AI agents. They're very quick to set up. There might be no setup at all. You might just be emailing Carly, for example. Next pro is what I'm calling adaptable. I'll explain why the quotes. Let me give you an example of what I mean by this. So, imagine instead of setting up this workflow, if the email is from Joe, label it as finance. I said to Carly, my AI agent, "Every hour, I want you to look at my emails and if it's from Joe, then label it as finance. Also, if there are other emails that are not from Joe, but they look like they're related to finance, go ahead and add that label to it." So, here what we did is instead of being forced to define all the conditions that would lead to a certain action, like who was the email from, we can just say, "Hey, use your judgment." And then the agent when it wakes up to run in an hour will go and read all the emails and think is this finance or is it not. Now the reason I have quotes in here is that anything an AI agent could do really can be done with a workflow if you add some LLM step to it which we will get back to but it would just take a trillion years because there are so many different cases that a human could not possibly write out every single one. It's much easier to say to Carly, hey, if it looks like finance, label it finance. Now, the cons are this is very expensive to run. Using AI agents which are running these AI tasks where there's prompts, there's tools, there's tokens being used. It is just like using ChatGPT. It costs a lot of money. It could also be really unreliable. AI is not deterministic. With LLM, that's AI on the other hand, it is totally probabilistic. So imagine we run the agent once and the first time it labels five emails as being related to finance. There are two receipts, there's a cash flow statement and two CFO emails. Imagine we ran the same agent again. In this case, it only labeled three of them as being related to finance. The cash flow one, just one of the receipts and one of the CFO emails. This is a very common occurrence because it is probabilistic what the AI, the LLM decides to do. In this case, it thought receipt one was related to finance and here it didn't. Now, this could be pretty disastrous in the case where it's not just a matter of labeling. Imagine you say to the agent, you should forward any finance related emails to my CFO and as part of the tools, it has your access to your email. Now, what if the LLM accidentally classifies a really embarrassing email as being a finance email? There is some nuance here that I am skipping over and the short version is that when you create one of these email agents, there are ways of restricting what it can do. You can block it from responding to people that the owner did not approve of. You can also pick which specific tools the agent has access to. But of course, the more of this type of setup you do, the less true it is that it is quick to set up. Next, we're going to move on to how you can have an AI component in your workflows. But I first want to make a quick point about terminology here. We've been talking about AI agents. There's something we have on Carly called tasks. What a task is is not a deterministic workflow. It's another agent that wakes up at a specific time. It has specific instructions and tools and then using those instructions aka prompts and tools, it will run. It will do its thing generating tokens, making tool calls, and then it will die. But when you hear tasks and when you're using tasks, you should think this is really an AI agent. But it's a special type of AI agent that has woken up at a certain time in the future. Now, back to deterministic workflows, aka traditional automations. Carly workflows that tab there are a bunch of different workflows. A lot of them are labeled no AI. This means they are the traditional workflows. They're deterministic. These are just the simple if then statements. But some of them do not have the no AI thing. What this means is there is an extra step in the workflow where there is an LLM called. But the point here is that not all your workflows need to be deterministic. You can have a data classification step that says is this a promotional email? What is this about? What folder should it go into? Now we can actually add this AI step. Send it to an LLM. Send that email body to an LLM and classify is this email about finance. Then the AI will say yes or no. If it is a yes, label it as finance. If not, leave it as it is. Now, you might be asking, is this going to make this way more expensive to run because thought the whole thing was it was cheap because there no LLMs and now you're telling me you can actually add AI to workflows. And the answer is it's still pretty cheap because if you notice this step is really small. There's a prompt and it's pretty short. It's just looking at one email and whatever instructions you had above. There's also no tools that saves a lot of tokens. This is a super small task. It can use a super cheap model. But the best part is it's also still very reliable. Why? Because you're still setting up the conditions exactly as you want them to happen. Now, for this step, it's true that if you run it the same thing twice because this is using an LLM, it might in one case say this random email is finance, but in another time it's not. So, that is still a risk. But at least you control the prompt that is used to decide. And now remember the case we were talking about where your CFO gets the really embarrassing email. Well, what we can do here is we could still have it be sent to the CFO, but we can add an approval step saying before you send anything, you have to email me and get my approval. So this way you can review every single thing. Let's review quickly what we've gone over. We've talked about automations and AI agents. Automations are workflows. They're mostly deterministic. In some cases, you might have a little AI classification task, but it's mostly if then. They're really cheap. They're very reliable because you know exactly what will happen, but they are setup heavy. AI agents are the opposite in many ways. They comprise of agents and tasks that your agents set up that wake up a new agent. They're super expensive to run, but they're very adaptable. They can do anything, and they are insanely easy to set up. Let's talk about some common problems. The first and by far most common is you have an agent or a task like a recurring task, but it should really be a workflow. Let me give you an example. Maybe I say to my agent, "Hey, every hour go through my emails and label the emails accordingly." This will cost you a fortune because when you use an agent, you're using a ton of tokens because it's holding that long system prompt with all the instructions and a bunch of tools that it doesn't need actually to label emails. What you should do is say to your AI agent, "Hey, make me a workflow." And the agent will be smart enough to know it needs an LLM step, the step that will classify the messages. And every time I receive an email, it should be filed accordingly. So, if you feel like you are burning money on tokens, the first place I would look is seeing if you can turn your tasks into workflows. Another problem is not spending time tweaking your workflows. There's a principle in computer science, garbage in, garbage out. So if you look at a workflow and say, "Why is it labeling all my emails as finance, for example?" It's because your inputs suck. It means probably your instructions are wrong or your deterministic rules are wrong. Now, the last one is kind of a catchall and cop out. But I see people get tripped up when they're not willing to just slow down and take a second and think about what needs to be true for this to be successful. For example, maybe I set up a workflow that will text me every time I get an urgent request. Are you thinking about the types of emails you're getting? Because you might be getting things that look urgent but are actually food delivery notifications and get excited because now anyone can really build one of these workflows. It used to be super annoying to do. You used to have to use things like Zapier and drag and drop and you'd have to do all this logic and it just sucked. And now with AI, you could just email Carly and say, "Can you set up this workflow for me?" So take a look at some of the resources we have down below. Thanks for watching and I can't wait to see what you build.