Automate Your Agency

AI Agents vs Automation: The 5 Differences That Actually Matter

• Alane Boyd & Micah Johnson • Season 1 • Episode 67

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Your automations are probably more fragile than you think—and costing you way more time than they should. Micah started the year convinced there was no real difference between AI agents and regular automation workflows. He was wrong, and now he's obsessed.

In this eye-opening episode of Automate Your Agency, Alane and Micah break down the five (plus a bonus) critical differences between traditional A-to-B automation and AI agent workflows that are revolutionizing how smart businesses operate. They reveal why your current automations break every time someone adds an apostrophe or changes a date format—and how agents eliminate that technical debt forever.

From chat interfaces that let you refine outputs in real-time to agents that can retry failed tasks and fix their own errors, this episode shows you exactly why the future of automation is here. You'll discover how agents access databases, handle unstructured inputs, and choose their own tools—just like training the perfect intern who never needs a coffee break.

In this episode, you'll discover:

  • Why traditional workflows require mapping every single scenario (and agents don't)
  • How chat interfaces transform automation from rigid to conversational
  • The database access that makes agents context-aware and incredibly smart
  • Real examples of agents fixing their own JSON errors and date format issues
  • Why you can stop building dropdown forms and rigid input requirements
  • How agents reduce technical debt and scale with your changing business

This isn't about replacing automation—it's about building systems that think, adapt, and grow with you. If you're tired of automations that break every time you make a small change, or if you want to build workflows in minutes instead of weeks, this episode gives you the roadmap to agent-powered operations.

Ready to explore AI agents for your business? Visit biggestgoal.ai for resources and tools to get started.

Disclosure: Some of the links above are affiliate links. This means that at no additional cost to you, we may earn a commission if you click through and make a purchase. Thank you for supporting the podcast!

For more information, visit our website at biggestgoal.ai.

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0:00:00 - (Alane): Welcome to Automate Your Agency. Every week we bring you expert insights, practical tips, and success stories that will help you streamline your business operations and boost your growth. Let's get started on your journey to more efficient and scalable operations.
0:00:18 - (Micah): So, Alane, for this episode, I was thinking back to the beginning of the year where I made a statement to you and everybody else on the team that basically said, I don't know the difference or I can't see the difference between an AI agent and just automating workflows from A to B with AI steps in between. And I think after I said that, it was my mission to figure out what the hell is the difference, because everybody else wouldn't shut up about it. And now we're in that everybody else mode.
0:00:53 - (Alane): I remember. I remember back then and I was like, well, you need to figure this out. Because I keep trying to get on webinars where they're talking about agents and they have. They are not explaining what an agent actually is. Like, this is ridiculous. So can we figure it out so we can decide if we want to do this? And luckily you did it and we all got to benefit from it.
0:01:15 - (Micah): Yes. And so for this episode, I think there's five key things that we can outline. And Alane, I'm going to need your help on this to keep it dialed down into plain speak. And, you know, not. Not the technical side, but the five differences between building systems and automations with agents. And I'm not only just saying it's. That's it. It's not just an agent. It is true workflows. It's going to have regular automation. It's going to have agents, it's going to have API calls. Right.
0:01:48 - (Micah): But, like, what can these workflows with agents do differently that we could never do before?
0:01:54 - (Alane): All right, let's break it down. What's the first one you got?
0:01:57 - (Micah): First one that I have is I feel like I should hold a card up that nobody can see if you're listening is chat interfaces. In a couple settings, you can add a chat interface to an automated workflow and you're good to go.
0:02:15 - (Alane): Yeah, Basically, that's the trigger where it's different than going to ChatGPT, because that is just going to the large entity that ChatGPT is and having a chat. This is different. This is a workflow based off a chat mica San chat interface. But if you just think of a chat that pops up and you paste or type something in, we have so many workflows that we kick off this way. Even internally. And I don't always love that we start off that way on some of them. But I'm going to tell you the reverse side, I love it for the right word isn't prototyping, but maybe just testing is like, do we like the outputs?
0:02:52 - (Alane): How are we working with it? You can also, it's a chat so you can re discuss things with the agent that's a part of that conversation. So it is really great for something quick and easy. I mean, we have so many of these that I've bookmarked that we've created for our internal processes. And it is fast to get an output and you can continue to conversate with it if maybe you want some edits to it or it didn't quite get what you wanted.
0:03:19 - (Alane): So it's great for just getting things out fast.
0:03:22 - (Micah): That's the key to me on this one. Alane. So I've got a great example. When we're building online courses in our community that's powered by Circle, it takes forever to come up with the modules, the lessons get, get it into Circle, refine all that, write the content, et cetera. So I think we've talked about this in the podcast in the past. We built an agent to help us with that. We built a workflow that contains an agent because it's not only an agent in this, but we. It starts with a chat interface. So we go to the, we go to the chat interface and we say, I would like to build a course on blank and I might upload a couple documents or an outline that I've brainstormed or anything like that to give it context.
0:04:08 - (Micah): The AI is then going to go out and process that and we'll talk a little bit more how it processes, where it gets the information outside of just its normal training data. And then it'll come back and it'll say, here's an suggested outline. Now at this point we can chat back to the agent and say, cool, but let's take out, you know, module two. Let's add a module about X. Let's refine module four. This way it goes back, it goes back to think, comes back, here's your updated thing. And we can go through this as if we're having a conversation with each other to brainstorm and define this whole outline. Now if this was an A to B workflow, it would be an input came in and it produced the list of modules and the list of lessons. There's no chatting, there's no refinement. We then have to go and do something with that. And if it's not right and if it's not good, we're screwed.
0:05:08 - (Alane): Yeah, we, we use it a lot of different ways. It's one of my favorite things that we've built, even just from call transcripts. We have a lot of chats that we've created to create the kickoff deck to recommend previous podcast episodes. Help create LinkedIn posts for ourselves. Like so many things.
0:05:28 - (Micah): Help create project briefs.
0:05:31 - (Alane): Yeah, so many different ways that we're using just this one trigger, I guess I'm not sure what to call it. It's a way of triggering the automation that is a two way street because you can communicate back and forth.
0:05:45 - (Micah): Okay, so that's number one, we have chat interfaces that we could never have before. Number two is it can access databases that are AI friendly. And so, you know, we've got a whole episode on this as well. But essentially you can take Google Drive folders, you can take SharePoint folders, you can sync that with an AI friendly database, and all of a sudden you can now provide context to these agents that have chat interfaces. So going back to our course examples, that course agent could go to a database on all the different services that we provide.
0:06:21 - (Micah): Now it can write the course and the course content around those services instead of just around its generic training data. Another example would be sops. Company policies. Have an agent that has access to all the SOPs, have a chat interface that gives individuals the ability to go, how do I do X? When Y occurs, it's going to go out to that database, it's going to pull the information, it's going to return it back, and you can have a chat and ask questions and you're literally having chats with your SOPs or your company policies.
0:06:55 - (Alane): Yeah, I mean, it works with chat, but it also can work with email triage, where the trigger isn't a chat, the trigger is an incoming email.
0:07:03 - (Micah): Yep, yep. And what's interesting about this is that. And we'll get to this here in a second. But it's, it's not just this one and done. Because it's an agent, it can go to the database and it can go, hmm, let me try a different query, let me try to pull it a different way. Is this what the user is looking for? And if it's an A2B workflow, we have none of that. It is. I pulled the data. That's the data you're getting.
0:07:32 - (Micah): You're not getting anything else. I hope this is right. In fact, it doesn't even hope it doesn't even try to guess. It is just like, boop. Here's your. Here's your response. Yep.
0:07:42 - (Alane): Yes.
0:07:42 - (Micah): And so if you know from an effectiveness point of view, being able to build quicker with chat interfaces and accessing databases is already huge gains. And we still have three more features and benefits that we can talk about.
0:07:57 - (Alane): All right, give me the third one.
0:07:59 - (Micah): The third one is it will retry things in different ways, much like the database example. It will try to query it in different ways, but also if it sees an error. So with agents, you can give it tools to work with. One tool might be go to the database, but one of the tools might be, hey, create a task in ClickUp. But let's say the configuration is wrong or it's looking for a task that doesn't exist.
0:08:25 - (Micah): ClickUp might return an error. The agent isn't going to immediately fail and go, well, tried that. I'm done. Yeah, did my job. In an A to B workflow, you're screwed. That's an error. The workflow stops unless you have an error handler and you have to. A human has to come in and try to resolve this with an agent. It will actually go in and try a few different things with the tool to figure out if it can based on the error that's coming back. It will try to figure it out.
0:08:58 - (Alane): I haven't done or experienced that part of it because I'm not on the building side. So what would be another example? So the ClickUp one was great, but what would be another scenario in one of them that we built, like with our podcast Cast agent, is there anything in there that it wouldn't have been able to complete and it would have.
0:09:18 - (Micah): Caught itself off the top my head.
0:09:20 - (Alane): Or maybe another example.
0:09:23 - (Micah): Yeah, I mean, things like if it couldn't find anything from a database. Right. If it returns zero results, well, can we search it differently? If it got an error, it can retry. And I'll give you this, this is getting a little bit technical, but let's say we have the. The AI writing structured data. So like JSON that would be sent to another platform. If that platform writes back and says, actually this is invalid JSON, the agent will see that and go, oh, okay, let me try rewriting it.
0:10:01 - (Micah): And it will try to rewrite the JSON to make it valid JSON and then resend it. And it will do that because the error message said this was invalid JSON. And so it can start to try to fix itself. Now we're not. When we're building these out we're not saying, if you get the invalid JSON error message, then do this. Which is what we would have to do in the deterministic like A to B workflows. We would go, this error came back, so then do this. This error came back, so then do that. This error came back, so then do this. And we'd have to map all, all of those situations out. And as we continue down this path, Alane, you'll like, you can visualize A to B workflows being like every single scenario, every single workflow, every single edge case, every single error, all of that has to get mapped out into this giant nasty workflow. You've seen them?
0:10:56 - (Alane): Gosh, yeah, I've seen what we've done. The way we've had to do it in the past and the way we do it now is very different night and day.
0:11:03 - (Micah): And when we have agents that can do it, we don't have to write all of those scenarios. We can give it guidance, we can give it instructions. But it can also, like we're talking about here, retry things in different ways based on the response that it's getting. Airtable is one. Sometimes when we connect with airtable it will be looking for a certain, like airtable has its own type of query language to pull stuff out of airtable.
0:11:31 - (Micah): So sometimes it will retry writing things in different ways to get the right query into airtable to pull the right information out. It's crazy.
0:11:42 - (Alane): Yeah, so it's really like a human in that aspect that it's like, oh, I didn't get it right. Oh, let me try it again. All right, that's cool. What's the fourth one?
0:11:51 - (Micah): The fourth one is that we don't have to be so rigid in the entire development of the workflow logic to produce the outcome. We can say, hey, this is what we're trying to achieve and then give it the tools and it's going to be outcome based. So again, a little bit like humans in that case, if it's A to B, typical workflow logic, we have to map, like I was saying a second ago, every single potential route, every single action, the order it's going to follow it specifically and if any, if there's any variance whatsoever.
0:12:29 - (Micah): Yeah, it's screwed.
0:12:31 - (Alane): So I got to see this. In like one of the outputs, we have a daily slack that goes out. And we didn't tell it how to format the information. We just say, hey, update us this with this information every day. And it created it and organized it in bullets, organized it in highest to lowest. And it added emojis. Like it already created this human aspect to it where, you know, we like using emojis. We want to have organize data. So it just did that on its own without us having to tell it. Do it like this. We want it this format.
0:13:03 - (Micah): Yep, A hundred percent. Now what you're talking about is an output. It's also very interesting on this side with inputs. So in like our normal A to B workflow logic, where we have to map everything out, you have to have structured data on the inputs. If that data is wrong, different, or not accounted for. Let's say something as simple as a date format changing. So we design a workflow from A to B and the trigger has input data that has a date field. And the date fields, you know, just say it's American, you know, kind of like month, day, year.
0:13:43 - (Micah): Mm. All right. But then we have European or Australian date format come in where it's day, month, year, and all of a sudden it blows up the entire workflow. Because wait a second, there's not a month of 20.
0:13:59 - (Alane): Right, right, right.
0:14:00 - (Micah): It's so dumb. Like a human would look at that and go, obviously that's the day and then the month and then the year. Well, some humans would be able to figure that out. But with the agents and with this style that input, we know it's a date, we can tell it it's a date. The outcome is deal with the date. And it doesn't matter if it's a UNIX timestamp, which is just a bunch of numbers. It doesn't matter if it's European formatting. It doesn't matter if it's American date formatting. It doesn't matter if it's slashes instead of dashes or periods or whatever, or if it's completely written out, it can handle all of those scenarios.
0:14:42 - (Micah): We used to have to work with this thing called regular expressions or regex. And writing that was like writing hieroglyphics sometimes. And it was very, very difficult. And now it's so nice to be able to say, hey, let's just give it some basic instructions in plain text. And then it can follow that without having to write all that rigid logic. What, what that all leads to is again, speed for development, but also the ability to have systems that scale with you. If your date format changes because you hired a new marketing manager and they like periods instead of dashes, your automation doesn't break anymore.
0:15:25 - (Alane): Yeah, less bugs, less errors coming through. The automation can continue to work.
0:15:30 - (Micah): Yep, yep. And you can start to see how all of these things can stack. So you have a chat interface. Well, doesn't matter what the date is, that format that you're giving, it doesn't. Can it access a database from the chat a hundred percent. If it sees. If it gets a little confused on the date format to carry on this analogy, it's going to retry different things while it's working through it. It's amazing.
0:15:54 - (Alane): That's really important when it comes to. Because that's really frustrating. You have an automation working, you want to make changes to your triggers or whatever it might be, but you want to make a change, you do it and then embrace the automation. And it's gets frustrating for anybody that whenever errors come in or it can't be executed because of a change.
0:16:16 - (Micah): Yeah. And this leads to coincidentally number five, which is that with agents inside your automation workflows, you can give them the tools to get the context that they need to be able to execute on the outcomes. We could never do this before. We had to. So I'll give you like an example. We've all lived in times where we had to fill out forms and those forms had to have dropdowns and those dropdowns had to have specific options because we needed that to be so defined that we could send that to automation so that we could write the logic to every option. If option one, do this, if option two, do this, if option three, do this, and the second one of those options changes, spelling, extra space, renamed, it doesn't matter what it is, the entire automation needs to be rebuilt. So to you, to the point that you were just making, Alane, every single time a process changes, a custom field changes, a name changes, a folder changes, a location changes, that meant you have to go to your automation team and go, well, I decided to change this now.
0:17:30 - (Alane): Yeah.
0:17:30 - (Micah): And sometimes it means a whole rebuild.
0:17:34 - (Alane): Yeah, it's really hard. And I remember one time, this has been a long time ago, I added an apostrophe to something and it broke everything big.
0:17:43 - (Micah): No, no, Alane.
0:17:44 - (Alane): Yeah.
0:17:45 - (Micah): Yeah.
0:17:45 - (Alane): I was like, holy cow, I should have just left the typo in there.
0:17:48 - (Micah): Yeah. So it was a typo. You added an apostrophe thinking that you're fixing it. And what happened?
0:17:55 - (Alane): It broke the automation. It couldn't work. It threw out an error.
0:17:58 - (Micah): Yep. Yeah. And so then we had to go back in, figure out what the problem was, fix all of that. And it was all because of a hyphen. But what we're dealing with now is because of all, because of all these things that we now can do with agents, and it can think through all these things. Thinking in a loose term, of course, is that we can give it tools to provide itself the context. So let's take the form with a dropdown. Very simple example here.
0:18:28 - (Micah): Why don't we allow the agent to pull all the options for the dropdown every time? Or better yet, we don't even need dropdowns anymore because it can handle real text processing in real language in just plain text. But whether it's choosing to categorize things or classify things in certain ways, we can provide it all the categories or we can give it the tool so it can go out and pull the information.
0:18:59 - (Micah): So really kind of an example of this would be, let's say we have an agent that needs to create new tasks in ClickUp. Well, instead of saying if client is A, then create task in B, blah, blah, blah, blah, blah, and map all that out, we can literally say, all right, agent, pull all the client folders out of ClickUp. Cool. Pull all the client projects out of any client folder, pull all the tasks available out of any client project, and suddenly it's building its own context. Picture of, well, here are all the clients. Cool.
0:19:37 - (Micah): Here's the email I got in. What email? What? You know, who is this email related to from this list of clients in our folder list?
0:19:47 - (Alane): Maika, you keep mentioning tools that you give the agent tools to use. Can you explain what you mean by that? Because when I first started hearing that, I did not follow until I saw an AI agent workflow and I was like, oh, that's what giving tools meant.
0:20:04 - (Micah): Yes. So the best way that I describe it, I think, Alane, you can tell me if this is horrible, but is to think of an intern. When you hire an intern, you give them a responsibility, you give them instructions on how to do their job, and then you give them tools. And those tools are going to be their email account, access to Google Sheets, Google Drive, Google Docs, et cetera. And within Google Drive and within Google Docs, they're creating new documents, they're editing documents, they're deleting documents, they're moving files into folders, creating new folders. They're doing all of those things.
0:20:40 - (Micah): Agents are exactly the same way. So you give it a responsibility, you give it instructions in the form of a prompt or multiple prompts. And the tools are almost identical. The only difference is you would give them specific aspects of those. So you could say, all right, agent one, you can create documents in Google Docs, you can edit documents in Google Docs, but you cannot delete. Those are the only things. You can't move them, you can't delete them, you can't do anything else. Your tools are creating and editing.
0:21:14 - (Micah): So it's another agent.
0:21:15 - (Alane): The tools would be the software platforms and what they can do in them. That's right, that's the tool. Okay.
0:21:23 - (Micah): Yep, yep. So whether it's email or sheets or docs or folders or you name it, name a platform, name a capability or a function within that platform and that is like one action that you can do is a tool that you can give the agent. Now you can give the agent multiple tools and this gets back to the ability to not have to write logic for everything. You can say, choose which tools fit the best for this job.
0:21:49 - (Micah): And when you combine that with the chat interface and accessing the databases and then looking at the tools that it has and even retrying things, it's now acting like a person kind of.
0:22:02 - (Alane): Yeah. And I'm going to add a sixth one and you've mentioned this throughout the five, but it needs its own one where the benefit of doing an AI agent workflow is really the speed at how we can do things. Because it doesn't need all the details. It can be hooked up to a database, it can have a chat interface. You don't have to tell it every single output. Just like I was saying with the example, we didn't tell it it needed to be bulleted and it needed to go from higher to lower and it needed to have emojis. Like we just said, give us the data.
0:22:34 - (Alane): It knew how to make it, how people can read. And so the. I mean there's been times, maika, where I've asked for something and we've had it in five minutes. I am not over exaggerating like five minutes. I had a working automation that could, could not be done with Make or Zapier in a traditional way way of doing it with the mapping of everything.
0:22:55 - (Micah): You could, you could, I mean technically you could make a five minute A to B workflow automation in Make or Zapier, but it wouldn't achieve the outcome that you're looking for.
0:23:05 - (Alane): Right.
0:23:05 - (Micah): What you're specifically talking about is an advanced desired outcome that would be next to impossible to automate in an A to B workflow. And yet even with all of that, we did it in five minutes.
0:23:19 - (Alane): Yeah. And we also really like building in platforms that are low code or no code because of that fact. It's faster, it's easier to maintain. There might be some pieces of custom code. Like you gave some JavaScript examples. Like there might be some aspects where we include some code, but that's when we would call it low code. But it really is being able to develop things fast, maintain them more easily, and not have so much technical debt.
0:23:46 - (Micah): Yeah. Oh, man, that's a great way to think about this and probably just a great way to kind of in this episode. Alane which is the bottom line of all of all the stuff that we talked about is the reduction in technical debt. When I mean, we built Make automations and Zapier automations for ourselves and for clients for years. And it was, you know, we did our best and it was the technology that we had at the time. But just like normal development, you incur technical debt. And for anybody listening who's unsure what that means, that means essentially when you build something out, you're creating the need to maintain it or fix it or adjust it, and you're gonna have to pay that back at some point.
0:24:33 - (Micah): So, you know, essentially you build it out and it's inflexible. So then you have to start making changes to the automation that you built. But now if you can build something like we've been talking about and you can make changes and you don't have to then edit the automation, you didn't have that technical debt.
0:24:55 - (Alane): Thanks for listening to this episode of Automate Your Agency. We hope you're inspired to take your business to the next level. Don't forget to subscribe on your favorite podcast platform and leave us a review. Your feedback helps us improve and reach more listeners. If you're looking for more resources, visit our website at biggestgoal.ai for free content and tools for automating your business. Join us next week as we dive into more ways to automate and scale your business.
0:25:21 - (Alane): Bye for now.

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Alane Boyd

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Micah Johnson

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