Almost Timely News: 🗞️ AI Agents 101
Almost Timely News: 🗞️ AI Agents 101
(2024-09-22)
Almost Timely News: 🗞️ AI Agents 101 (2024-09-22) :: View in Browser
The Big Plug
I've been on the road a ton, and I've got two brand new talks that I've never done until recently.
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100% of this week's newsletter was generated by me, the human. You will see AI usage and outputs in the video version of the newsletter as I demo building an AI agent. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future.
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What's On My Mind: AI Agents 101
This week, let’s do a deep dive into AI agents. You’ve heard a lot about these recently from companies like Salesforce and Hubspot, all the things they can supposedly do, all the power they will allegedly unlock. How much of the talk is hype versus real, and how will you get started on your AI agent journey?
Part 1: What Is an AI Agent?
Let’s start with the bare basics. What is an agent? An agent is infrastructure around an AI model that lets the model communicate with the world around it.
Think of an AI model - like OpenAI o1 or GPT-4o, Google Gemini 1.5 Pro, Mistral Nemo, etc. - as the engine of a car. Incredibly powerful, comes in a bunch of sizes for different applications, very capable. Some engines you’re allowed to modify (open models), others not so much.
A model is the heart of AI. These things cost millions of dollars and hours to make, by training them on crazy amounts of data. But you never use a model directly. In the same way that Microsoft Word is a piece of software written by humans for human use, AI models are pieces of software written by AI for AI use.
We use models through an interface, something like ChatGPT or Claude or Poe, or within a service like Slack or Salesforce. These interfaces are essentially the rest of the car - the seats, the wheels, the windshield, the gas tank, etc. You never drive down the road sitting on an engine - you drive down the road in a car or truck.
And like a car, a consumer interface like ChatGPT does nothing without us to drive it. Open up ChatGPT and stare at the empty screen and it will not do anything. It won’t take action on its own, just as our Toyota Prius in the driveway doesn’t go anywhere without us getting in and turning it on.
Suppose we wanted our vehicle to do something on its own. We’d need a self-driving car or other autonomous vehicle, one that had instructions for what to do, one that could drive itself.
That’s what an agent is. It’s a different kind of infrastructure around an AI model, one that has greater autonomy to perform a specific task.
Think about an agent like a self-driving vehicle with a specific purpose. A commuter electric vehicle (EV) isn’t a pickup. A pickup isn’t a commercial tractor or plow. You wouldn’t drive a John Deere X9 1100 industrial combine to work (or you shouldn’t, anyway), nor should you plow a field with a Prius. The Prius engine might be able to do some light plowing, but the rest of the vehicle is unsuitable for that purpose.
In the same way, while an agent has a very powerful generative AI model at its heart, it still has clearly defined infrastructure around it that guides its purpose, and that infrastructure dictates what it should be used for.
What kind of infrastructure? An AI agent's infrastructure looks a lot like any other app. There might be connectors to external data sources, talking to APIs upstream like your CRM. There's likely code, middleware that performs specific tasks in sequence. That code will talk to an AI model of some kind, and then when results come back from the AI model, there will be more code to send the results somewhere else. Finally, there will be some kind of scheduler or triggering mechanism that will tell the agent to do its thing.
I'll give you an example of an AI agent I built. I built an agent to score blog posts. The agent's components are:
A Python script to grab URLs from an RSS feed and extract the main page content, storing it in a SQLite database
An R script that talks to the SQLite database, feeds each blog post, one at a time, into a language model along with the Trust Insights Ideal Customer Profile
The same script takes the scores returned by the language model, stores them back in the database, and then makes a bar graph of the scores over time
This agent is manually triggered, but once it was written, it's something that can just run and do its thing without my intervention. It can't do anything else EXCEPT this task, but it does this task well.
If this sounds like an app, it is. "AI Agent" is just fancy, expensive language for a self-driving app.
Part 2: Why Do We Care About AI Agents?
The reality is that consumer tools like ChatGPT scale poorly. You can, and should, have things like prompt libraries where you can share and improve prompts, but ChatGPT still requires a human to operate it.
What if you wanted to take a high performing prompt, like writing a monthly report, and have a machine do that task autonomously? Instead of you having to download the data, clean the data, pass it to an AI model, and copy paste the results into an output format, you’d have an agent perform that sequence of repetitive, predictable tasks instead.
Agents are a natural evolution of the most repetitive generative AI tasks, where you have clearly defined processes and outcomes that are as predictable as the sun coming up or the phases of the moon.
That part, the repetitive part, is critical. Agents are best suited for repetitive tasks with predictable outcomes, where minimal human supervision is needed. Language models give us far more flexibility than traditional agents - anyone who’s ever used old-school chatbots on websites know how little flexibility the past generation had - but are still best suited for tasks with clear outcomes. It’s a poor use case for an open ended chatbot, for example, to run on your website where people can have conversations about anything. (Not to mention expensive)
How do you know what’s a great task for an agent? Here’s a simple benchmark. If you use generative AI in a system like ChatGPT and you’re just copy pasting prompts and copy pasting outputs with very little thinking or review needed, you’ve got a task that’s ideally suited for an agent.
That SWOT analysis you run quarterly where you just copy paste data, upload files, copy paste the prompt, and then copy paste the results into a slide deck? That’s 100% an agent task that can and should run autonomously. The final product should just appear in your inbox.
The example earlier of scoring blog posts? Doing that one at a time, copy pasting the post in, and copy pasting the results out? Giant waste of time for a person to do that. Having AI do that with the help of code around it? Perfect use case to unlock the power of AI at scale.
A lot of people think AI Agents are just chatbots, in the same way they think of ChatGPT as just a blog post writing tool. Yes, they can do that. But using it just for that purpose is like taking a Harrier to the grocery store. It can do that, but it's capable of so much more.
What are some common AI agent use cases? It depends on the industry. In marketing, these can be things like shopping assistants, price optimization, consumer-facing chatbots, customer service, etc. Here's the easy rule of thumb: if it's a valuable prompt today, it's an agent tomorrow. Someone will wire up the necessary infrastructure to turn that high-performing prompt into a self-driving app.
Part 3: What Does the Agent Ecosystem Look Like?
As we’ve seen and heard at major conferences and trade shows this fall, everyone and their cousin is rolling out a low-code/no-code agent framework. Salesforce has AgentForce. Hubspot has Agent.ai. There are dedicated platforms like Poe, Character.ai, etc. that are low-code or no code.
There are advanced, heavy-code platforms like Google Vertex. And in the various marketing technology landscapes, there are hundreds more agent vendors. All of them promise the same thing - powerful, easy to build, easy to deploy agents. Many of the no-code ecosystems offer pre-build agents that you can simply pick up and use, in the same way that you use any SaaS service.
All of the SaaS agent platforms have the same purpose: to get you to buy (or stay) with their platforms by fully automating agent creation and management. In some ways, it’s another form of vendor lock-in - everyone wants you to build agents in their ecosystem, on their platform, so that it's harder to leave.
Here's the thing: remember that AI agents are nothing more than self-driving apps. That means that like AI models themselves, you can build agents entirely within your own ecosystem and infrastructure. If there's a use case for generative AI that you feel is valuable enough to turn into an agent, then there's good reason to consider building that agent in house as opposed to having it live in a third party's ecosystem.
How do you decide whether to buy or build? It comes down to whether or not you have the people, skills, and infrastructure to support the agent or not. Again, they're just apps. If you already self-host apps at your company, then you can just as easily self-host AI agents.
Part 4: How Do You Build an AI Agent?
You’re probably at least 50% of the way towards your first agent. Let’s go through the evolution of an agent. We start with basic generative AI, with prompts in tools like Claude and ChatGPT. You use these tools with prompts. This is old hat by now.
Over time, you should be systematizing your use of prompts, with things like prompt libraries. These are stock prompts you’ve built that create high-performance outcomes, and these prompt libraries are things you share with your team at your company. For example, at Trust Insights, we have a ton of shared prompts in our Google Drive that we can just drop right into Gemini whenever we need to. And because they're nothing more than Google Docs, anyone can edit them, upgrade them, etc.
After your prompt library, you might get to a point where you want to turn your prompts into hosted apps, using custom GPTs (ChatGPT), Gems (Gemini), or artifacts (Claude). These apps are sophisticated prompts and background information that are designed for a specific task like writing Google Ads or analyzing competitive corporate strategy. They’re tuned for that specific purpose. They're not self-driving - a person still has to operate a Gem, a GPT, or an artifact, but they're very close to an agent. A user who picks up one of these apps doesn't have to do any prompting, they can just get right to work.
It’s at this point where you would convert the Gem/GPT/artifact into an agent. Instead of you, the human, using the app, you might convert the app into an agent that can retrieve the appropriate data, pass it through a generative AI model, and then send the outputs somewhere for other use.
How do you do this? It's at this point where you would decide to use a third party platform or build it yourself. The best way to make that determination is to use the Trust Insights 5P Framework; we should define the purpose, people, process, platform, and performance of our agent and how it evolves.
First, the purpose: what is the AI agent supposed to do? What outcome should it generate, what problem should it solve? If you're extending the work you've already done with GPTs/Gems/Artifacts, then you should have a pretty clear idea of what this is. If you're starting from scratch, take the time to write out some user stories and do requirements gathering - the same as you would for building any kind of app.
Second, once you have a robust list of requirements, take a look at your people. Who will be using this agent? Just internal employees? The general public? That's important to know. Equally important is to determine what skills your agent needs. If you've identified that the agent will need serious coding, do you have those skills in-house? If not, then you'll almost certainly need to use a third party platform.
Third, it's time to look at your current processes, your current prompts and AI. Do you have specific requirements you need to meet, standards for compliance, data privacy, etc.? The same standards you use for regular, human-driven generative AI will apply to your agent, so if you have to use AI solutions with a certain level of data privacy, that will in turn help you choose your AI agent strategy. You can't reasonably put an agent on a hosted service that doesn't meet the same standards as the models themselves.
Once you've done these steps, then you can start evaluating what platform you want your agent to live on. As with all AI, remember that choosing the technology and platform comes last, after you've worked out the requirements for everything else.
Finally, decide how you'll measure the performance of your agent. At a fundamental level, it obviously has to work. Additional KPIs will be determined by your purpose; if your goal is to serve X number of customers, then you can measure that and determine if that's working as intended. If privacy is a critical concern, then you measure how much data is leaking in your ecosystem. If marketing is your purpose, then you measure marketing activity like how quickly customers are advancing through the customer journey. Whatever your purpose is, measure it.
In the video edition, I walk through the building of a simple agent based on my language translation system instructions.
Wrapping Up
AI agents are a natural evolution of our generative AI use cases. If we're already getting benefit from our human-driven AI usage, getting benefit from our agents is the next logical step in the process.
I would add that for minimum frustration and maximum value, you don't want to skip straight to building an agent most of the time. Instead, think about using tools like Claude, Gemini, and ChatGPT essentially as R&D tools to develop the use case for an agent, prove the concept, iron out the bugs in your prompting, and only then build it into an agent. You'll save yourself a tremendous amount of time and headache that way.
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ICYMI: In Case You Missed it
Besides the recently updated Generative AI for Marketers course I'm relentlessly flogging, this week, I released a 5 part series on AI and music, originally recorded at the 2024 Marketing AI Conference.
Fireside Chat: Music and AI, Part 5: Hungry Eyes on Charing Cross: AI Composition in Action
Fireside Chat: Music and AI, Part 3: The Ethics of AI Music: Consent and the Future of Musicians
Fireside Chat: Music and AI, Part 2: The Human Touch: Imperfections and Intent
Fireside Chat: Music and AI, Part 1: Defining Music in the Age of AI
Almost Timely News: 🗞️ Next Generation AI Models (2024-09-22)
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Generative AI for Architecture, Engineering, and Construction
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Predictive Analytics and Generative AI for Travel, Tourism, and Hospitality, 2024 Edition
Building the Data-Driven, AI-Powered Customer Journey for Retail and Ecommerce, 2024 Edition
The Marketing Singularity: How Generative AI Means the End of Marketing As We Knew It
Empower Your Marketing With Private Social Media Communities
Paradise by the Analytics Dashboard Light: How to Create Impactful Dashboards and Reports
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Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you're looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.
Business Development Manager - Resilient Analytics at Stanley Consultants
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Events I'll Be At
Here are the public events where I'm speaking and attending. Say hi if you're at an event also:
Women in Construction Operations, San Francisco, September 2024
MarketingProfs B2B Forum, Boston, November 2024
Social Media Marketing World, San Diego, April 2025
Content Jam, Chicago, April 2025
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My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.
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See you next week,
Christopher S. Penn