So What? How to Build an AI Agent

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In this episode of So What? The Trust Insights weekly livestream, you’ll learn what an AI agent is and how you can use it to improve your content marketing. You’ll see how to build an AI agent from scratch using the five P’s of marketing. You’ll also get a look at a real-world example of an AI agent in action. This livestream will equip you with the knowledge to start building your own AI agents.

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In this episode you’ll learn:

• What is an AI Agent?
• What are the use cases for an AI Agent?
• How to build one to assist with your marketing

Transcript:

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

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Katie Robbert 00:30

Well, hey, everybody. Welcome to so what, the marketing, analytics, and insights live show. I am Katie, joined by Chris and John. How’s it going, guys?

Christopher Penn 00:37

Hello.

Katie Robbert 00:39

Oh, so close.

Christopher Penn 00:41

I don’t have long enough.

Katie Robbert 00:43

Yeah, I was going to say, Chris, your arm doesn’t quite reach the end of your screen, whereas John, yours disappears.

John Wall 00:48

Into the screen with the over. I’ve over-zoomed.

Katie Robbert 00:53

This week we are talking about how to build an AI agent. So, we’re going to cover what it is, what it does, how to use it — all that good stuff. And so, I guess the appropriate question is, Chris, what the heck is an AI agent?

Christopher Penn 01:06

That’s a great question, Katie. Let’s talk about this in terms of the evolution of how we’re using generative AI. So, in the beginning, everyone starts with a chat GPT window, or a Gemini window, or whatever. We all start there. We all start talking to these magical tools. We start doing a lot of copying and pasting, and we eventually get to the point where it’d be really nice if we could maybe have a version of this that just does one thing really well.

And so, a lot of the tools have this sort of evolution with something like a gem. Or, a GPT or Claude calls them artifacts. But these are now gone — have gone from a broad generic tool into a very specific use case that it’s locked into.

Christopher Penn 01:58

You still have to operate it — you still have to do the thing, but it’s better. It certainly is easier to scale because you can just give a coworker access to your KDGPT, for example, and that coworker can then use it to create stuff. Well, the next evolution in this process is for you to take that logic and that specialization and make it autonomous — make it do its own thing.

Essentially, if you think of AI — an AI model — like an engine, it’s like putting the rest of the car around it. So, it has to be able to go out and get data, maybe, and process that data, and then hand it to AI to do something with that data, and then do something with the results from the AI. So, that’s in a nutshell — that’s kind of what an agent does.

Christopher Penn 02:49

It is essentially, I like to call it, a self-driving app.

Katie Robbert 02:55

Okay. I mean, you lost me for a little bit there with the car thing, but you brought me back with the self-driving app, I think. Well, I mean, and to be fair, I know what an AI agent is. And so, I’m asking the question to get us all on the same page, us and the people who are watching the show.

And so, in a nutshell, it sounds like an AI agent is a piece of software that is driven by generative AI. You have to give it data. You have to give it something to work with. But once you do, it’s like, okay, here’s my stuff to work with. Now, you — the person — work with me, like, do your thing.

Katie Robbert 03:36

Whatever it is you want the AI agent to do, it will do within the confines of whatever data you’ve given it.

Christopher Penn 03:44

And so, ideally it goes and gets

Katie Robbert 03:46

The data itself, but you have to specify that. And so, when you’re saying like a self-driving car, for example, a car theoretically doesn’t just turn on and take itself somewhere — I hope — but a person has to actually still be in control of driving the car and saying this is where I want you to go. This is how I want you to operate. And the car doing 99% of the work — there’s still human involved to give it direction, to give it instruction.

Christopher Penn 04:22

That’s right. That’s right. And it’s — and agents are very similar. Now, here’s where it gets tricky. There’s as many use cases for agents as there are generative AI. So, it’s not just a one specialization.

If you can think about doing it with generative AI, you can have an agent do it as well because you’re basically just adding the infrastructure around what you already use AI for. So, in terms of thinking about use cases, and how do we deploy this stuff, anything you currently use generative AI for today, an agent can do semi-autonomously. And, the reasons that you would use this are two.

Christopher Penn 05:02

Number one, if you take the time and go through the process, you will dramatically reduce mistakes and encapsulate your best practices, kind of like what you do with the GPT, but now in a piece of standalone software. And two, it’s about scale. Scale is the reason you use agents because when you use something like a GPT or a gem, and when you fire up KDGPT, you — the human — still have to be feeding at stuff and doing stuff, which means that if you needed KDGPT to do something 1,000 times, you’re going to be there a while.

Katie Robbert 05:38

So, I personally, and maybe I’m thinking about this wrong, I personally don’t want a car to drive me — like I don’t want a self-driving car. I want to still be in control of driving the car. So for me, maybe an AI agent isn’t the right thing. I might still just want, like, a custom GPT that I interact with. Am I thinking about that the right way?

Christopher Penn 06:04

It depends on the use case. So, I figured today, as an example use case, we would build an agent that would use the ideal customer profile that you built for Marketing over Coffee, the show. And by the way, if folks are interested and you want an ideal customer profile of your own, Katie will build it for you. We’ll put the little contact thing down at the bottom.

However, we’re going to make an agent that will take the Marketing over Coffee, ICP, and then score every episode of Marketing over Coffee to see how well aligned that episode is to what the ideal customer profile would find helpful or useful. So, that’s the idea behind this agent, is you would tell the agent to go, it will go and get the episodes, it will go, and we’ll provide it.

Christopher Penn 06:54

The ICP will provide it a scoring rubric. It will go through all of them, all the episodes. And it will score each one. And it will tell us how well, on average, Marketing over Coffee is doing, meeting the needs of its ideal customer profile. This is a great use case for an agent because there’s a lot of shows, there’s a lot of episodes, we would want to have a machine doing this as opposed to us copying and pasting the transcript from the episode — 100 episodes — that, just one at a time, over and over again. That’s not a good use of our time.

Katie Robbert 07:27

You ready for this, John? Do you want to take guesses on? Let’s say, let’s put our stake in the sand now of what we think the average score is going to be for a Marketing over Coffee episode because it’s not like you haven’t been doing this a long time. It’s not like you don’t know your audience.

John Wall 07:43

Yeah. No, but I’ll. Yeah. I’ll put a chip down that the rubric will say 70%, that slightly above average. That’s.

Katie Robbert 07:50

Yeah, I’m going to give you more credit than that. I’m going to say that it skews more towards, 80%. Like, give yourself a solid B. And the only reason I say that is because you can’t be everything to everyone. And, I think that’s something that’s a whole other topic.

But when we’re creating content, you can only appeal to so many people at a time. And so, I feel like that’s true of your episodes, where sometimes it’s really going to resonate with this segment of your audience, and sometimes it’s really going to resonate with a different segment of your audience. But I think overall, you know your audience really well.

John Wall 08:28

Yeah. It’s interesting. But this is great. It’s right at the heart. It sharpens the whole thing. I was, because I’ve been doing checking into some of Tom Webster’s research. And he was talking about how a lot of podcasts have actually, two or more audiences. You have — but you do have a primary audience that’s the one, that’s the ICP group.

So, it’ll be interesting to see if that, the ICP aligns with what we think is the primary. And then yeah, but then there is this whole idea of secondary and tertiary audiences. The example that he gave is My Little Pony. Like, you have kids that love My Little Pony, but you also have the Bronies, which is a completely independent audience, not related, nothing in common.

John Wall 09:08

But of course, the toy company wants to sell to the kids. They’re not really interested in the Bronies. So yeah, we’re. We’re going for the children.

Katie Robbert 09:17

All right. So, before we go down that unpleasant rabbit hole, let’s go ahead and start building this AI agent and not get too far distracted.

Christopher Penn 09:28

Yes, please. We’re going to start, unsurprisingly. Oops. Hey. Hey.

Katie Robbert 09:35

We both hit buttons. That’s. We have to have one button pusher, not multiple button pushers.

Christopher Penn 09:40

There we go. We’re going to start with the five Ps. So, for this agent, the first thing we have to figure out, and we’ve pretty much discussed, is the purpose of the agent is to find out if Marketing over Coffee is making content the ICP wants. So, that’s pretty clear. And John’s case, John would say, as the owner of Marketing over Coffee, I want to assess and score the Marketing over Coffee content for alignment with my ICP so that I can make content that better appeals to my ICP. Increase audience size and retention and all that stuff. So, the pretty straightforward user story, that’s part one. Part two, we’re going to need, which we already have. So, we’re going to skip over this part, is the ICP itself, because we’ve done that a bunch of times now, but we have the ICP.

Christopher Penn 10:33

So, that leaves us with process and platform. And the way to build this is to start from the ground up. And by that I mean we want to make sure that we can do this within generative AI first before we go building an agent because there’s no point in building an agent for something that doesn’t work.

Let’s say we have something like our AI of choice, and we would say something along the lines of we want to build a scoring rubric from an ICP. So, I’m going to start without a prompt that says, “From this ideal customer profile,” and I’m going to attach a copy of it here — there’s my stuff. I’ll put in the Marketing over Coffee ICP. “I want you to build a scoring rubric that will assess whether or not an episode of the show serves the needs of the ICP.”

Christopher Penn 11:31

“The input will be a transcript from the user. The rubric should use variable integers to score each item, such as 0 to 30 to 50. Seven rubric should total 100 points. Build the scoring rubric.” And this is going to create us a, essentially, a scorecard.

And then we would give this scorecard to our agent at some point. We first want to know if this even works. So, let’s go ahead and go over to marketingovercoffee.com and let’s pick the most recent episode here and take sort of this content from the page. And remember this is just prototyping. This is just getting the basics in place and say, “Using the scoring rubric, score this content for alignment with the ICP.” And I’m going to put that in and let’s see how it goes.

Katie Robbert 12:39

See, John, we always say it, this is where you and I need to have some, like, pre-planned banter or jokes or something while we’re waiting for the machines to run.

Christopher Penn 12:49

Yep. This score to 14, it says it is very poor alignment with the ICP, lacks depth in areas crucial to the target audience. Sponsor integration feels forced. The overall content likes actual insights and cohesive narrative and things. So, that’s essentially the ICP is saying this could better.

Now, part of the reason for that is that post is just show notes. There’s no transcript on the page. The transcripts there probably score much better because there’d be a lot more information to work with. But at least we know the general process here works. Now we have to take into account what did we just do. We had to go get the post contents. We had to feed it to an AI model. We had to write a prompt. We had to build a scoring rubric, and then ultimately get the information out.

Christopher Penn 13:36

So, those steps in the process are now what we need to think about building an agent around to do if we agree that this is — if we agree that this works. We now have to think about building an agent.

Katie Robbert 13:50

So, what’s interesting, I remember building the Marketing over Coffee ICP. And so, it’s a — when you said it scores a 14 out of 100, I was like, “That seems off.” And one of the things, just to backtrack a little bit, the way that I put together the ICP process was you always start with a goal. So, what is the goal of the ICP? And I still think this is a useful exercise, but I just want to make sure that everybody has the right context so they don’t suddenly think, “Oh, Marketing over Coffee, like, they’re just kind of doing their own thing. They don’t care about the audience.”

The — I believe the goal for this ICP was a little bit more skewed towards the sponsors to understand them versus the audience.

Katie Robbert 14:45

So, I just want to give that context so that when people who are watching this are like, “Well, what the crap? Marketing over Coffee.” Because we’re using it as an example. But I also want to protect the brand reputation of Marketing over Coffee. So, I wanted to sort of give that a little bit more detail about the ICP that we didn’t show you that we built as well.

So, just sort of a general disclaimer, we already know now, sort of that we have a little more context that the scoring isn’t going to add up to, like, 100%.

Christopher Penn 15:15

Right. So, that list of things, get the post contents, assess it with the language model, store the scores, make a report. So, those are all individual subtasks. Of those, only one of them is something that generative AI does. The rest is not generative AI. The rest is — it’s not even classical AI. It’s just good old-fashioned IT. It is databases. It is an RSS reader. It is probably a scheduler of some kind.

So, we’re going to need to gather all those pieces for today’s cook. We’re going to be using a local model because of the cost, mainly, and a local database. This is — this is an example. This is not how you would build this in production.

Christopher Penn 16:04

In production, you would have a, way more requirements gathering. B, certifying that all the vendors and all the pieces in the chain meet any compliance requirements, etcetera. And C, wouldn’t run on just one person’s laptop because that’s a terrible idea.

How do we do this? How do we start figuring this out? The first thing we need to do is we need to figure out what the pieces are that we need. We have our ICP. We now have our scoring rubric. Those pieces are good. We’re in good shape there. We need to build some code.

Now, before I start building code, I want to say there are a lot of agent vendors on the market — so Agent AI from HubSpot, Salesforce’s Agent force, IBM’s guidance architecture. Everybody’s got an agent architecture of some kind.

Christopher Penn 17:03

And all of them, unsurprisingly, would like you to invest and pay money for them. I’m going to build this as an example with 100% stuff that’s made from scratch. Just like you could make everything from scratch for a cup of clam chowder, or you could just buy a can of soup. I’m going to do things intentionally the longest way possible.

Katie Robbert 17:29

So, and I think that’s fine. I think where people — and we’ve been having this conversation over in our Slack group, Analytics for Marketers, which you can join, TrustInsights.ai, analytics, four marketers, you can totally join for free, no obligation — I think when you start to get to saying things like, “And now I’m going to write code,” that’s where a lot of people get intimidated by building your own AI agent, for example, because it’s like, “Well, I don’t know how to code. I don’t know what that means. How do I know if the code is correct?” And we’ve talked about this in earlier versions of generative AI of — you know how to get around that.

Katie Robbert 18:11

But I think giving explanation as to what you’re doing when you’re doing it is going to be helpful because I think this is where people get stuck. This is where people get intimidated and, like, let me go buy something off the shelf, even if it’s not exactly what I want.

Christopher Penn 18:29

And I think for non-technical folks, as long as you don’t have any data compliance things — meaning you’re not using sensitive data, you’re not using protected information, or confidential information — if you’re doing something like this where we’re just using Marketing over Coffee, it’s very public information, then using cloud-based stuff that’s somebody else’s no-code architecture — totally fine.

There are a good number of days when I don’t want to make the clam chowder from scratch. I’m just going to go buy it from the store in the nice, leak-proof package can. There are situations where if you are doing this, say, with protected information — maybe you’re going to build an AI agent to review EHR records, electronic health records — you’re going to make that from scratch.

Christopher Penn 19:13

Because you have to, or at least good chunks of it, because you have to obey compliance and regulations.

Katie Robbert 19:19

Okay. So, let’s say we have a scoring rubric. We have the bits and pieces. What do we do next?

Christopher Penn 19:29

Next is the part that I hate the most, and that is requirements gathering. We have to think through, what do we want this thing to do? And so, I have taken the time to write this out because this would take forever in a day on a live stream, and no one wants to watch this.

But I started to think, okay, well, what does it got to do? It’s got to get the data, it’s got to process the data, and it’s got to do something with the data. So, three basic steps. So, a piece of code would have to do those three steps. And then, so it’s step one, it’s got to get the data. How does it get the data? Well, Marketing over Coffee is a podcast. Podcasts have RSS feeds. RSS feeds contain the information about the episodes.

Christopher Penn 20:16

So, we could have a piece of code going out to Marketing over Coffee — just grab the RSS feed, which has all the show information in it. That’s a helpful and useful thing to have available to us. So, we’ll tell the code, “Hey, I’m going to give you an RSS feed. I want you to go grab it, but here’s what I want you to do with it. I want you to grab it, and I want you to store it in some kind of database so that we have it.”

So, that’s what this first requirement is. Here is “Go get this thing, and you’re going to store it in a database.”

Next requirement: “You’re going to look in the database and make sure that things that are in the feed, if there’s stuff that we have already grabbed, don’t grab it again. That’s a waste of time.”

Christopher Penn 21:00

“If something doesn’t work, tell us.” And then once you’ve got all the data, then move on to step two. What we’re doing in this section of requirements gathering is not only gathering requirements, but we are also starting to write code. We’re just writing it in plain language, not in a particular programming language.

We go to stage two. “Okay. Now you’ve got the data. What do you do with it? Well, here’s how this thing’s going to work. It’s going to be a Python script. And I want to say — I’m going to give you an ICP, and I want you to do stuff with that ICP. I’ll put the prompt in the code as to how to use the ICP. You’re going to talk to this particular language model. Today, I’m using Mistral small because it runs on my laptop.”

Christopher Penn 21:48

“Again, use the model you want, and then post by post you’re going to score it using our scoring rubric and then update the database to say, ‘Here’s the score’ — put in the database. Then when you’re done with all the processing, then it’s time to move on to the third step. Third step is reporting. Make me a bar chart of all the episodes and their scores and put a trend line on it and make it HD. So, 1920 x 1080, 8 dpi. Use these fonts.”

Now here’s some considerations: “Error handling should be graceful. You should make an error log so that we have audit trails, etcetera.” This is — this is the — believe it or not — this is the hardest part of the entire thing because you have to sit here and think through what are all the things I want this thing to do.

Katie Robbert 22:36

So, I have one question, and then I have some tips for people watching this. So, what do you mean when error handling should be graceful? What does that mean?

Christopher Penn 22:50

Graceful error handling, within the context of using AI to write code, means your code should not just fall over and die. Your code should attempt to resolve the error if possible. And if it can’t, tell the user, “Hey, here’s what happened and here’s where things went wrong.”

Katie Robbert 23:06

So, the machine understands that particular phrasing. That’s not just — you’re not just saying, “Put on some lace gloves and say, ‘Oh, dear, it didn’t work.'” It’s actually a command in software development that the machine understands. Yes, because what I had a software

Christopher Penn 23:24

Development jargon. I was like, “Okay.”

Katie Robbert 23:28

The second thing — I know it sounds sort of very meta — but you can use generative AI to help you write the requirements. So, if you say, “Hey, whatever system, I want to build a thing, help me write some requirements.” It’s probably going to start asking you questions like, “Great. What do you want it to do?” You say, “I want to do this thing.” It’s like, “Okay, how does it get from point A to point B?”

So, Chris, you’ve done requirements often enough to know that you have to go from step A to B to C to C to D, and so on, and so forth. Someone who’s newer to this kind of system to building an AI agent and to writing down detailed requirements might not know the level of detail you need to go to. So, use the tools that you have.

Katie Robbert 24:16

Work with a generative AI system to walk you through how to build requirements so that you have requirements to build a thing. I know it sort of sounds like very silly, but that’s something you can absolutely do while you’re getting ready to use AI in a different context.

Christopher Penn 24:36

Exactly. And I even ended by saying, “Based on these requirements, what questions do you have?” Because I am sure I have forgotten something. So, let’s go ahead. I’m going to flip over. I’m going to be use Claude 3.5 Sonnet for this.

And the reason for that is, on coding tests, Sonnet has performed the general best of the different generative AI models. There are leaderboards such as the one at lmarina AI that allow you to choose different classes of tasks and see which models score the highest for those tasks.

So, even within cloud hosted tools like Gemini, and Chat GPT and stuff, and Claude, it’s a good idea to know what the strengths and weaknesses of individual models are and use the right model for the right.

Christopher Penn 25:19

So today we’re going to use Claude. I’m going to start by saying, “Today we’re going to write some Python code. First, here are some Python best practices. Confirm okay, when you have read them.” And what I’ve got here is just a big pile of Python best practices. It’s essentially “you should use this syntax, use this type of thing.” We want to prime the model to tell — like you should do. You should follow very specific best practices.

This is your coding standards for your organization. So, whatever your company’s internal coding standards are — if you’re develop — you have developers — you would use it here. If you don’t have any, then you would use the Trust Insights PAIR framework to build it — to say “Today, we’re going to, I want you to walk me through what are the best practices for writing Python code?”

Christopher Penn 26:11

Have it build the list, and then you can copy and paste it in. So, we’ve got this lovely pile of requirements, all set of guidelines to say. “Next, let’s start having you review the requirements. I can’t type today for my code. And that long text document that we just went over is what goes in.”

Claude’s going to read this through, and it’s going to have a whole bunch of questions like, “Hey, there’s some things in here that you are not super clear on. What should we do?” They’ll ask like, “I see you’ve got some — you know, what are some details on the language model, etcetera?” And I will say, “Here’s all of my answers because we’ve done this dance before.” I can go through and answer each question, say, “Okay, I want you to create the database.”

Christopher Penn 27:10

“The RSS feed should be a command-line argument.” I forgot this. I’m going to give you some example code so that it’s clear what you’re doing. In fact, let me give it the example code here. Here is the example code. And so, this will answer the major questions the model had.

This is the compressed sort of cooking show version. You will have to manually think through, “Okay, well, what is the answer to this question? How should we do this?” And so on and so forth.

Now, with that background information, Claude is starting to write out the actual Python script — the actual python code for all these pieces. Once it’s done this part, you’ll then have to — and you should answer questions because it’s going to have some more questions for you.

Christopher Penn 28:03

It will spit out in the left-hand side window, like, “Hey, it looks like there’s some things here that I’m still unclear on. Do you want — what do you want me to tackle next?” Let’s see what it says. It says, “Implement the score article and the generator port.” Let’s go ahead and — go ahead and build the score article function.

So, what Claude is created for so far is the skeleton framework of this code. I bring it into a code editor, like Visual Studio Code. We can see it’s chosen libraries. It’s set up a database for us. It’s built something to read the Marketing over Coffee RSS through any RSS feed — not just Marketing over Coffee. There’s a placeholder for the AI portion — for the generative AI part. There’s a processor, there’s a reporting engine, and then there’s the main function.

Christopher Penn 28:55

So, all this stuff is what Claude has created. Katie, to your point earlier, you don’t necessarily need to know how to code the exact syntax. You should be able to read code and say — if you want to do it, this very technically — should read the code and go, “Okay, I can see what’s missing,” or “Wow, that really doesn’t look right,” or, “Huh, I think we forgot something. We should probably put in,” say, like, “Oh, I don’t know, the scoring rubric. We seem to — I seem to have forgotten that there, that needs.” And then I can go back to the model and say, “Oh, I forgot to do this, and I forgot, got to do that. Please put this in.”

Katie Robbert 29:30

Well, and I think that’s one of the things that people forget about using generative AI is that it’s smart enough to know to ask more questions. Very rarely have I ever used or set up an instance where it’s been like, “Okay, great. Nope. I’m good. I’m going to do exactly what you tell me.” But I think it’s also making sure you ask — you put in your prompt — “What questions do you have?” And, or ask questions along the way.

That is such a great pro tip, because then it’s going to — it’s really going to dig deep and thoroughly into the information you give it and say, “Well, as you know, the world’s smartest computer, I see you’re missing the following five things because I’ve now read everything about this topic, and I know exactly what’s supposed to happen, and you’re missing stuff.”

Katie Robbert 30:22

So, use it that way. Like, I think we, the humans, think we need to know exactly what we want or need or have all the information before we even approach generative AI. And that’s just not true.

Christopher Penn 30:37

Exactly. So, again, cooking show style, I have gone through this conversation chain many, many times because it produced a couple of hilariously bad errors. However, we eventually did get there in terms of a piece of code that

Katie Robbert 30:54

Actually works. But were the most graceful. Yes. Did you get the bow in the white-glove service?

Christopher Penn 31:02

I did get one close. One thing that I do strongly recommend you do is, once you’ve gotten some code that’s working, you ask it, “Please give me the functional and domain requirements for this code.” And what that does is it will summarize: “Here is everything that we’ve done in a handy requirements format that you can then keep with your code so that when you go back and look at this in six months,” you go, “What does this code do?” “Oh, here’s all the requirements.” And you put it right in the code.

This has the added advantage, if you’re going to be doing this with code, of when you hand it back to a language model, the model can go, “Okay, I know what I’m doing. These requirements are very clear.”

Katie Robbert 31:48

Makes sense. I mean, I’m not disagreeing that requirements are the foundation of literally everything.

Christopher Penn 31:55

Okay. So, again, cooking show style, we have our code. There is a section to handle logging. There is a database creation part. There’s a part that will read an RSS feed that we provide. There is a connector now to. I’m running Cobalt CPV using the Mistral small model. This is an AI model. It’s a generative AI model, like Chat GPT, but it runs on your computer.

Cobalt is probably the least friendly version that you could possibly use, but it is the most technically sophisticated if you are not technical. What I would strongly recommend is a free platform called Ollama, which has an adorable llama mascot. And this is a piece of software that will install on your computer and then allow you to choose a variety of models to run.

Christopher Penn 32:48

Your computer needs to be pretty nice to run some of these models. So, we’ll have more information on the Trust Insights website in a little while as to how to make the decision. But if you can play video games on it today and like, nice ones, like Call of Duty, then you can run most of these models.

Katie Robbert 33:06

Yeah. Not like Candy Crush. That doesn’t count.

Christopher Penn 33:09

No. That doesn’t count. Okay. So, we have our code. There’s the interface to Cobalt. Here’s our prompt:

“You are a content marketing expert. You know this, this. Your task is to examine the following blog post and evaluate how likely it is the ICP would find the blog post helpful based on their needs, using the content scoring rubric. Here’s the ICP which we’re going to provide based on the ICP. Use a scoring rubric to determine whether or not it’s helpful. Here’s the scoring rubric which we’ll provide. Here’s the blog post, which is going to pull from the database. Execution instructions: don’t write code. Return only the final integer score,” and so on and so forth. “Follow these instructions specifically.”

Christopher Penn 33:48

So, this is going to now read that database and line by line, row by row, take the Marketing over Coffee posts and score them against this. After that, it’s done. With that, it will update the database, and then it should make a nice, lovely bar chart. It may not, but it might. And then the triggers, and then of course the requirements. So, are we ready to see if this even works?

Katie Robbert 34:18

I think we’re as ready as we’re ever going to be. Yeah. What about you, John?

John Wall 34:21

Let it rip.

Christopher Penn 34:22

Let it rip. All right. Let’s see here. I’m going to start a new window.

John Wall 34:34

Yeah. And I’m going to put some engine revving up sound or something.

Christopher Penn 34:42

All right. So, here we go. Oh wow. Okay. So, the code is downloaded, and now it’s starting to pull the database. So, here’s the most recent episodes we got to the RSS feed. Okay. And these are Marketing over Coffee episodes.

And now it’s running and scoring the individual articles. And you can see, on the right-hand side of the window, what the AI is actually doing. You can see every prompt going in and then returning back the numerical scores. So, this is actually working surprisingly well, which is shocking to me.

Katie Robbert 35:25

I mean, if you’re teaching people how to do this on a live stream, I would hope that you would know what you were doing. So, stop being surprised that the thing that you built actually works.

Christopher Penn 35:33

No. I’m a terrible coder. You know this.

Katie Robbert 35:36

But here’s the thing, and I want to make sure that this is clear for people who are trying to approach this themselves: You used AI to help you make sure that the requirements were clear. You said, “Here’s what I need you to do. What questions do you have?”

And this is the point I was trying to make earlier, is even if you’re not a coder, the generative AI will walk you through what steps need to happen because it knows you just have to give it direction. It’s sort of back to the, you know, self-driving car analogy that we started with. Yeah, it can do 90% of the work, but you, the human, still have to tell it where the heck to go. And that’s what you did, Chris. You said, “This is where I want you to go.”

Katie Robbert 36:17

And it said, “Great, here are my questions. Now you’ve given me answers. Okay, now I can go ahead and drive.” And that’s what it’s doing. So, I want to sort of highlight that because that is where a lot of people get stuck, is: What do you mean, I have to code? But this is what it looks like.

Christopher Penn 36:34

Yeah. In fact, to your point, I did not do any actual writing of Python code. I put my fingers on the keyboard to generate Python code. I did instruct the language model. “Here’s the kind of code that I want written.” And we obviously walked through all the different pieces of it. But so far in all of this, I have not actually written any code.

Katie Robbert 36:57

Right. But you did do solid requirements, which is, again, that’s the foundation of any project — especially software development — because you have to tell the machine what the heck you want it to do with specific details.

Christopher Penn 37:12

Exactly. So, as it’s doing its work, it’s hard to tell, it’s hard to eyeball. But I do see an awful lot in the 70s and 80s. So, I think, in terms of where this nets out, it’s probably going to be — it’s going to be close as to which of the two of you is correct.

Katie Robbert 37:30

Well, and, back to the context that I wanted to give, I did find the original purpose statement of the ICP that we built for Marketing over Coffee. And so, it is a little bit misaligned with the way that you’re using it today. So, I just want to be realistic with people in terms of, like, the scoring. I want to protect — very protective of the Marketing over Coffee brand.

So, it’s not that it’s misaligned with its audience. The context in which we’re using it is a little bit misaligned with the example that we’re giving.

Christopher Penn 38:01

Right. Which means that if we wanted to change this — and this is what’s nice about this particular methodology — very little is actually in the Python code. We are specifying the ICP separately, the scoring rubric separately, and so on and so forth.

So, all those pieces you can then give to a completely non-technical person and say, “Modify this.” Like, “Okay, this is the ICP. I want you to modify the ICP and do it differently.” Okay.

So, with that, what we end up with is this. So, this is the final output from the code. Once it finishes, you end up — well, you end up with two things. One, you end up with a lovely database. I’m using the SQLite database. You can see all the scores there, the date of the crawl data score. And then the code generated this lovely graph — this lovely graph.

Christopher Penn 38:53

If were to draw a general trend line through the red section, probably be somewhere around 60-ish for this. When we go back — this is now two years worth of episodes, which is in the feed.

Katie Robbert 39:08

What do you think, John?

John Wall 39:10

Yeah. It’s interesting on a bunch of friends. There’s all kinds of — the one thing is, there are episodes that have transcripts and those that don’t. And I don’t, off the top of my head, know the breakdown of those.

But it does seem that some of the ones with transcripts definitely score higher. And then, yeah, you definitely hit the thing about, when we did the ICP, were talking about sponsors, not versus listeners, but, I think the good thing across the board is, yeah, it’s steady. You know, when you look at a trend line, it’s like, okay, it’s a consistent level. It’s not completely all over the place from 0 to 70. And there’s a couple that are hitting the 90 mark, too. So, there are, and yeah, relatively few.

John Wall 39:55

I’m not going to call out the guests where we got an 18, and all that kind of stuff. But that we can do offline.

Christopher Penn 40:02

But you know who those are — all the winners.

Katie Robbert 40:04

Yeah. Well, and so, John, let’s say, for all intents and purposes, you’re the CEO of Marketing over Coffee. If Chris were to hand this analysis to you, would you say, “Okay, I can do something with this. Let’s take a look at the episodes and do less of those that scored lower, and then take a look at the episodes that scored higher.” Is that the kind of next step that you would want to see? Or what would you, as the CEO of Marketing over Coffee, want to do next now that you have this analysis?

John Wall 40:34

Yeah, like, any analysis. It just opens up a whole another box of questions. I’d definitely be interested in, like, the high-low end of it. Like, take the five worst. What did they all have in common? And try and look at that. And the great part is, we can use a prompt to do that. You know, you can just say, “Hey, grab these five. And what do they have in common versus the other ones that hit?”

So, yeah, it’s definitely the kind of thing you want to put your hands on and get into and see if you can get to more specific — as far as, like, “Yes, do more of this,” or, “No, do more,” you know, “Do less of that.”

Christopher Penn 41:08

Yeah. Now, we’re not going to names of the low end, but these are from this particular ICP — the sponsors ICP, which is what we’re talking about. These are the sort of the top five. We have direct mail, Morty Oberstein of Wix. We have Cassie Bruno. We have two from Ginny Dietrich. So, those are the ones that resonate best for this particular ICP.

John Wall 41:30

Oh, that’s interesting. So, all of those are guest episodes. None of those are the topical shows that we’re doing where we’re covering the thing of the week. And so, that’s interesting because, really, there’s a complete overlap in some of those vendor versus sponsor. There are many show guests who are also sponsors because those audiences overlap completely. So, that’s interesting.

Christopher Penn 41:53

Yep. So, now some additional things that we could do to augment this code. Number one, this is a Python script. It lives on, right now, on my desktop. It’s right there. I could put in a schedule and say, “Run this every Monday,” and it would pick up the new episodes, score them every Monday, just constantly do a thing. We could also say, “Send John an email,” like, maybe every Monday, “send them the last four scored episodes,” or maybe make that bar chart. If it’s the end of the month, send John a new copy of the bar chart. Just email it to him. We could push the data into something like Google’s BigQuery and then have it be a dashboard that John could just look at anytime he wanted to see the data.

Christopher Penn 42:37

But the AI agent, once you turn on that scheduler, it does its thing. So, our work here is done. It now is fully self-driving, and we just tell it when to run.

Katie Robbert 42:51

I like that. You know, I’ll be looking for some kind of an email about our podcast, as it aligns to our ICP now. I mean, that’s always how this goes. Like, you give an example. I’m like, “Great, when can we have that for ourselves?”

Christopher Penn 43:10

Exactly. And so, all the individual pieces, too. You can also swap out, if you want to use Google’s Gemini instead of Mistral small, you can tell the code, “I want you adapt the code to work with the way Gemini works, or with Chat GPT.” If you want these OpenAI’s models, you can totally do that.

Again, this is not sensitive data, so it’s your choice. You can change the output. You don’t like the bar chart, you want a line chart, fine, tell the AI of your choice, “Rewrite the code to do this thing.” But you have an agent, and the agent can be made to be autonomous.

Christopher Penn 43:45

You can set — if you have it running with, like, cloud providers, like OpenAI, whatever — you could take that piece of code and put it on a remote server somewhere so you don’t have to keep your laptop on all the time. Or if you’re like me, you’re traveling somewhere, you don’t have Internet access that day, the code can still run somewhere in the cloud.

And everything that we did, up to the point of writing the code itself, is still stuff you have to do, even if you use a low-code or no-code platform. All the gathering, the ICP, the rubric in this particular instance, the requirements gathering — you have to do that, no matter.

Katie Robbert 44:20

What you have to — I mean, but that’s always been true, whether it’s the ICP and the rubric or whatever. You can skip requirements, but then when things don’t work, you have a harder time going back to find out where it didn’t work. And that’s where software development goes wrong a lot of times because you spend a lot of money trying to figure out what broke versus spending the time upfront, before you even touch the software, to figure out what it should do.

Christopher Penn 44:51

Exactly. So, to recap today’s episode, why agents? It’s all about scale and reducing errors — making sure your best prompts are encoded in a system that runs itself. It’s a self-driving app. We did the Marketing over Coffee episode scanner, and we built out the entire thing using the five Ps: the purpose, the people provided by the Trust Insights ICP. Our process — we decomposed it into all the individual tasks, and then mapped those tasks to different technology components. We built our own platform from scratch because it’s the best way to show how to think through this stuff. And then at the end of the show, performance, the fifth P, did we get the outcome? We did. We have a lovely chart as the outcome. So, that’s building an AI agent and how you build one.

Katie Robbert 45:41

Great. Now everyone go do it. That’s your homework.

John Wall 45:44

Go build some stuff.

Katie Robbert 45:45

Go build some stuff.

Christopher Penn 45:47

Go build some stuff. Any final thoughts, you two?

Katie Robbert 45:53

Other than I have a lot of use cases that I would like to see us do in the company to revise our own reporting? Just try it out. I mean, I think that’s the big thing with all of this stuff is just try it out, see what happens, see where you get stuck, and actually use generative AI to help you get unstuck as well. So just go. Go play with it. Go experiment.

John Wall 46:18

Yeah. It’s way too much work to try and manually compile this kind of stuff. So, put the machines to work for you so you can get some additional insight into what you need to do.

Christopher Penn 46:26

Yep. And absolutely shameless plug: If you absolutely, possibly just don’t want to do this — no surprise, we do. Yeah. Exactly.

All right folks. That’s going to do it for this week. We will talk to you all on the next one. Thanks for watching today. Be sure to subscribe to our show, wherever you’re watching it. For more resources and to learn more, check out the Trust Insights podcast at TrustInsights.ai/tipodcast and our weekly email newsletter at TrustInsights.ai/newsletter.

Got questions about what you saw in today’s episode? Join our free Analytics for Marketers Slack Group at TrustInsights.ai/analytics-for-marketers. See you next time.

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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.