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How Vertical AI Agents Are Built

By
Mark Freedman
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0

Now that we’ve explored what Vertical AI Agents are and the tools that power them, let’s dive into how they’re actually built. Unlike generic AI models, designed for broad applications, Vertical AI Agents are tailored for specific industries and optimized for real-world use cases. Building one involves several key steps, from defining its purpose to deploying it into an automated workflow.

Defining the Problem and Industry Focus

Before building a Vertical AI Agent, the first step is to clearly define its purpose. Unlike general AI, which tries to solve a wide range of problems, a Vertical AI Agent must be laser-focused on a specific task within an industry. For example, you wouldn’t build a Vertical AI Agent to write poetry and detect fraudulent transactions. That would be like hiring a famous chef to fix your car.

Key Questions to Ask:

  • What business function will this AI automate or improve?
  • Is there enough industry-specific data to train the AI effectively?
  • Does the AI need to integrate with existing company workflows?

Example Use Cases:

  • Healthcare: AI that helps radiologists detect abnormalities in X-rays.
  • Finance: AI that flags potentially fraudulent transactions in real-time.
  • Retail: AI that predicts which products will sell best in different seasons.

The better you define the AI’s job upfront, the more useful it’s going to be. No one wants an AI that just makes educated guesses of what to do.

Collecting and Preparing Data

Data is the fuel that powers AI. But unlike broad AI models that use massive general datasets, Vertical AI Agents need high-quality, industry-specific data to specialize in their task.

Data Sources for Training

  • Structured Data: Spreadsheets, databases, transaction logs
  • Unstructured Data: Text documents, images, audio recordings
  • APIs & External Feeds: Real-time financial data, legal updates, medical records

Data Cleaning and Preparation

Before AI can learn from data, it has to go through a serious cleanup. You know the term, “garbage in, garbage out”? Well, that’s not just true for junk food and bad reality TV (Survivor is good — don’t fight me on that). AI is the same way. If you train it on messed up, inconsistent data, don’t be surprised when it starts spitting out nonsense.

This means removing duplicates, filling in missing info, and making sure all the formats match up. It’s like making soup. If half your ingredients are expired, you wouldn’t just throw everything in and hope for the best, right? AI is the same. It can’t magically fix bad data, so we have to clean it up first.

Oh, the soup I make is canned, so there’s that…

Choosing the Right AI Model

Ok, so now we have clean data. Now what? We need to pick the right AI model. Not all models work for all tasks. You wouldn’t use Google Maps to write an essay, right? Well maybe geography essays, but I digress…

Where Different Models Fit in Vertical AI:

  • Large Language Models (LLMs) like GPT-4o, Claude, and Gemini
    • Great for anything text-based (contracts, chatbots, customer emails).
    • Example: A legal AI agent reviewing documents might use Claude, known for longer context windows, or GPT-4o, optimized for fast, accurate responses.
  • Vision Models like OpenAI’s DALL·E, Google’s Gemini, Stable Diffusion, and even Midjourney
    • Best for image generation and computer vision tasks (medical imaging, quality control in factories). Models like Midjourney are mainly used for creative image generation, but could help in design-heavy industries.
    • Example: A radiology AI might use Google’s DeepMind models for X-ray analysis, while a fashion AI might use DALL·E for virtual clothing design.
  • Custom Models & Fine-Tuned Engines
    • For companies that want something really specific, like an AI that can detect tax fraud but only for restaurants (because yes, that’s a thing).
    • Example: A fraud detection AI might not use GPT-4o but instead fine-tune a financial anomaly detection model on proprietary transaction data.
  • Hybrid Models (RAG + API Integrations)
    • Sometimes, workflows mix and match models. Like a chatbot that pulls live product data using an API. It’s kind of like a Swiss Army knife, but for AI.
    • Example: A customer service chatbot might use GPT-4o for conversation, RAG to pull knowledge from an internal database, and an external API to check inventory in real-time.

Examples of When to Use Model Types?

(As of this writing)

Model TypeBest Use CaseProsCons
GPT-4oGeneral-purpose AI Agents (chatbots, research assistants)Strong reasoning, fast responses, multimodal (text, images, audio)Can be costly for high-volume tasks
Claude (Anthropic)Long document analysis (contracts, research papers)Handles long-context better than GPT-4oLess powerful for complex reasoning
Gemini (Google DeepMind)Image and text AI, integration with Google productsMultimodal strength, good for web dataNot as widely adopted as OpenAI
Fine-Tuned ModelsIndustry-specific AI (medical, finance, legal)Tailored for a specific job, cheaper to runRequires proprietary data and training

How to Choose the Right Model for a Vertical AI Agent

  • Define the problem: Is it text-heavy, image-based, or predictive?
  • Decide on real-time vs. batch processing: Does it need instant responses or deep analysis?
  • Evaluate cost vs. accuracy: Can a general LLM work, or does it need fine-tuning?
  • Consider integration needs: Does it need API access to external data sources?

Types of AI Models Used in Vertical AI:

  • Supervised Learning: When “labeled data” is available, which basically means examples that have both the input and expected output defined. For example, training an AI to identify fraud using past fraud cases. I remember reading about a bank that trained its fraud detection AI on old data, and it kept flagging legit transactions because consumer habits had changed. AI is only as good as the trends it learns from. And, if you’ve ever looked at Twitter (especially “X” ?) trends, you know that’s a terrifying thought.
  • Unsupervised Learning: When patterns need to be discovered, like customer segmentation in retail.
  • Reinforcement Learning: When AI learns through trial and error. It’s like training a dog — give it a treat (for AI, some more tasty data to chew on) when it gets something right, and it will learn to repeat that behavior.

Many companies don’t build AI models from scratch. Instead, they fine-tune existing pre-trained models using their own domain-specific data. APIs and cloud-based AI services can make this process more efficient.

Training the AI Agent

Therefore, training AI means feeding it data and allowing it to learn patterns. This process typically involves:

  • Batch Training: Training the AI in steps to adjust its accuracy.
  • “Hyperparameter” Tuning: Tweaking model settings to improve performance. Fancy word… Think of hyperparameter tuning like adjusting settings on a camera. Too much brightness, and the image is washed out. Too little contrast, and details disappear. (I asked my wife about this one. She’s the professional photographer ?). AI training is full of these small adjustments.
  • Performance Testing: Checking how well the AI performs on new, previously unseen data.

Tools Used in AI Training:

  • TensorFlow / PyTorch: Open-source AI frameworks for model training.
  • AutoML Platforms: Google AutoML, Microsoft Azure ML, Databricks AutoML, and AWS SageMaker for training without extensive coding.
  • Custom Training Pipelines: Built-in enterprise AI systems tailored for specific use cases.

Once the AI reaches a good accuracy level, it’s ready for deployment.

Deploying the AI into a Workflow

A Vertical AI Agent must integrate into a company’s workflow and existing tools to be useful.

Ways to Deploy AI Agents:

  • As an API: AI runs in the cloud and serves results via API calls, like fraud detection as a service.
  • Embedded in Software: AI is built into business applications, such as AI-powered chatbots in customer service tools.
  • Standalone AI Assistants: AI works as a separate tool, like an AI-powered document reviewer for legal teams.

By embedding AI into workflow automation, businesses can maximize efficiency and create seamless, repeatable AI-driven processes. I find this to be one of the coolest uses of AI. It’s like having a digital assistant that never sleeps (and doesn’t need coffee — thinking of how much money I’d save!).

Even for personal tasks, workflow automation can help simplify the more painful tasks we have to perform in life. For example, I’m currently working on automating an existing, documented, manual workflow for preparing tax return breakdowns for my accountant. Look at the date — I better hurry! It’s a bunch of work now, but I can then do this in a fraction of the time starting next year.

Continuous Learning and Optimization

AI isn’t a “set it and forget it” tool. If industries evolve, the AI needs to, also. This means retraining it with fresh data, fine-tuning its accuracy as new trends emerge, and, of course, making sure humans stay in the loop to catch weird mistakes before they spiral out of control.

Companies use workflow automation to keep AI running smoothly. The circle of AI life — using AI to help AI — really not as scary as it sounds. For the foreseeable future, I believe humans will remain the clearinghouse for all AI processes. As long as we don’t forget how to think, you know, with AI doing all that thinking for us these days.

AI can update itself constantly, but humans need to sleep, eat, and take vacations. Yes — we need vacations ?. Workflow automation helps AI keep running 24/7 without burning out — unlike us!

Wrapping Up

Building a Vertical AI Agent isn’t just about training a model. It’s about integrating AI into a specific industry, fine-tuning it with the right data, and making it a seamless part of real-world workflows.

To recap:

  1. Define the problem and industry use case.
  2. Gather and clean data to ensure quality.
  3. Select the right AI model for the job.
  4. Train the AI using machine learning techniques.
  5. Deploy AI into a workflow so it delivers real business value.
  6. Continuously update and improve the AI to keep it relevant.

Okay, that was a lot of info. But, if you’ve stuck with me this far, you now know that building a Vertical AI Agent isn’t just about throwing data at an algorithm and hoping for the best. It’s about giving AI a real job, setting it up with good data, picking the right tools, and making sure it keeps learning.

Next time, we’ll skip the theory and look at real-world AI Agents already making a difference.

Until then, be polite to our future overlords. They’re making a list, and checking it twice…jk

The Tools That Power Vertical AI Agents

By
Mark Freedman
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0

So far, we’ve talked about what Vertical AI Agents are and how they learn. But AI doesn’t just train on its own. It needs the right tools to become useful. That’s where Retrieval-Augmented Generation (RAG), APIs, and workflow automation come in. These technologies help AI learn faster, stay up-to-date, and work efficiently in real-world applications.

Just a quick note — I’m writing these articles as if the reader may not be familiar with several of these terms, or if they aren’t in the software field. Even if you have this experience, you may pick up something new.

Retrieval-Augmented Generation (RAG) – AI’s Research Assistant

AI is only as good as the data it’s trained on. But what if you need it to understand a company’s intellectual property (IP) or other proprietary information? Also, what happens when the world changes? AI models trained months ago might give outdated answers. This is where Retrieval-Augmented Generation (RAG) comes in.

How RAG Works

  • Instead of relying only on pre-trained knowledge, RAG allows AI to retrieve new information from external sources (like databases, APIs, or the web) when answering a question.
  • It’s like an AI that can “Google” things before responding, making sure it has the latest information.

Example of RAG in Action

Imagine a legal AI assistant that helps lawyers draft contracts. Laws change all the time, so instead of only using outdated training data, RAG enables the AI to pull the latest regulations from a legal database before generating a response.

APIs – AI’s Building Blocks

APIs (Application Programming Interfaces) let different systems talk to each other. This is often how developers integrate systems with either third-party or other internal services. They’re also like bridges that connect AI to external data, tools, and applications.

Why APIs Matter for AI

  • Access to external knowledge: AI can fetch real-time stock prices, weather updates, medical records, etc. via APIs.
  • Access to internal systems: AI can also fetch real-time company data, or integrate with existing internal systems via APIs.
  • Faster AI development: Instead of training a new AI model from scratch, developers can use APIs to access pre-trained AI models for tasks like speech recognition, image analysis, or language translation. In other words, AI models can make use of other, third-party, AI models as well.
  • Better integration: AI doesn’t work in isolation. As hinted at above, APIs allow AI models to be embedded into apps, websites, and enterprise software.

Example of APIs in Action

Imagine a customer support chatbot that helps users troubleshoot problems. Instead of training an AI to recognize every possible issue, it can use APIs to:

  • Pull customer data from a database.
  • Check inventory levels if a replacement is needed.
  • Retrieve help articles from a company knowledge base.

This makes the AI smarter and more useful without requiring endless retraining.

Workflow Automation – AI That Powers Business Processes

AI models don’t just exist. They need to be deployed, monitored, updated, and, in many cases, integrated into business workflows. Vertical AI Agents often play a key role in workflow automation, where they help automate specific, repeatable company functions. Instead of just acting as isolated models, they become part of an entire automation system that streamlines operations, reduces manual effort, and improves efficiency. Workflow automation helps AI run smoothly without requiring human intervention at every step. This helps free people up to focus on their actual creative and intellectual roles.

How Workflow Automation Helps AI and Businesses

  • Data Pipelines: Automates the process of collecting, cleaning, and feeding new data into AI models.
  • AI Model Updating: Instead of retraining manually, workflow automation triggers model updates when new data becomes available.
  • Real-Time Decision Making: AI can be integrated into business workflows, automatically acting on new insights.

Example of Workflow Automation in Action

A fraud detection AI at a bank needs to stay updated as criminals and hackers develop new tactics. Workflow automation ensures:

  • New fraud cases automatically get added to a training dataset.
  • The AI model retrains itself periodically.
  • The AI’s fraud detection logic updates in real-time, reducing false positives.

Without automation, AI would require constant manual updates, making it inefficient and costly. But beyond updating AI models, workflow automation ensures that Vertical AI Agents can be embedded into core business processes; whether that’s automating customer service workflows, optimizing logistics, or improving financial reporting.

Bringing It All Together

So to build a powerful Vertical AI Agent, you don’t just need a well-trained model. You need tools to keep it smart, connected, and adaptable:

  • RAG keeps AI up-to-date by retrieving the latest information from internal and external resources.
  • APIs give AI access to external data and services for real-time insights and data.
  • Workflow automation ensures AI keeps improving without constant manual intervention.

These technologies transform AI from just a trained model into a fully functional, continuously learning, and self-improving system.

Next time, we’ll cover how Vertical AI Agents are built, discussing the steps, tools, and processes involved.

How Vertical AI Agents Work

By
Mark Freedman
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0

Now that we’ve talked about what Vertical AI Agents are, let’s take a look at how they actually work. These AI agents don’t start out intelligent. They have to be trained, just like people learn skills over time. But instead of sources like teachers and YouTube videos, they learn from data — often a massive amount of data. This training makes them really good at one job, whether it’s spotting fraud, assisting doctors, or even recommending new songs.

Just a quick note — I’m writing these articles as if the reader may not be familiar with several of these terms, or if they aren’t in the software field. Even if you have this experience, you may pick up something new.

Feeding the AI – Where Data Comes From

So before a vertical AI can do its job, it needs information. Imagine trying to play a new video game without ever looking at the rules. You may make some educated guesses based on general knowledge you’ve gained playing games over the years. But you’d really have no idea exactly what to do. AI is the same way; it needs examples to learn from.

Where Does the Data Come From?

  • Healthcare AI: Medical images, patient records, doctor’s notes
  • Finance AI: Transaction logs, fraud reports, stock market trends
  • Retail AI: Purchase history, customer behavior, inventory levels

Retrieval-Augmented Generation (RAG) is a method that helps AI find the best and most useful information from huge amounts of data, improving what it learns. We’ll dig into this a bit more in the next article.

Cleaning the Data: Getting Rid of the Mess

AI can’t learn properly if the data is a mess. Garbage in leads to garbage out. Imagine trying to read a book full of spelling mistakes and missing pages. It wouldn’t make sense. That’s why data has to be cleaned before AI can use it. Cleaning means:

  • Removing duplicate or useless data
  • Filling in missing information
  • Making sure all data follows the same format

Training the AI – Learning from Examples

Once the data is ready, it’s time to teach the AI. There are different ways AI can learn, just like us.

Three Ways AI Learns:

  1. Supervised Learning: The AI is given “labeled data,” meaning it’s told what’s right and wrong. Think of a teacher grading homework and showing students their mistakes.
  2. Unsupervised Learning: The AI is given data but not told what’s right or wrong. Instead, it finds patterns by itself, like a kid sorting Legos into colors without being told how.
  3. Reinforcement Learning: AI learns by trial and error, like playing a video game and figuring out what works based on scores and rewards.

AI developers often use pre-trained AI models (like using a pre-made cake mix instead of baking from scratch). APIs (Application Programming Interface) allow AI to access these models to learn even faster. We’ll talk more about APIs in a future article. They have multiple uses.

Fine-Tuning – Polishing AI’s Knowledge

Training gives AI a good starting point, but it’s not perfect. It needs fine-tuning to do its job well. This step is like practicing for a test — learning from mistakes and improving over time.

How AI Gets Fine-Tuned:

  • Adjusting settings to improve accuracy (we’ll talk about what these settings are in a future article)
  • Testing on new, unseen data
  • Removing biases that could cause unfair results

Many AI systems keep learning even after they’re deployed. With workflow automation, AI models can keep updating themselves without needing to be re-trained from scratch every time new data appears.

Putting AI to Work – Deployment

Once AI is ready, it’s time to put it to use. There are different ways to use an AI system:

  • Inside apps or software (for example, an AI that detects fraud in banking apps)
  • As an API (used by a chatbot that helps customer support teams)
  • Standalone programs (like a medical AI that scans X-rays for doctors)
  • Inside existing tools we use daily (like Slack, WhatsApp, etc.)

Continuous Learning – Keeping AI Smart Over Time

Just like we need to keep learning to stay sharp and relevant in our field, AI needs updates too. If an AI is trained on old data, it might make bad or outdated decisions. To remain useful, it must keep learning from new data.

How AI Stays Up To Date:

  • New training data helps it stay accurate
  • Feedback from users helps it adjust and improve
  • Error checking helps fix mistakes and biases

As with initial training, AI systems can use Retrieval-Augmented Generation (RAG) to pull in the latest information whenever they make decisions. This helps them avoid outdated answers and stay fresh.

What is Inference?

Once AI is trained, it needs to use what it learned to make decisions. This is called inference. It’s when AI looks at new input and predicts an answer based on what it was taught.

  • Example: A fraud detection AI is trained on thousands of real fraud cases. When it sees a new transaction, it infers whether it looks like fraud or not.
  • Example: A medical AI learns from millions of X-ray images. When it sees a new X-ray, it infers if there’s a problem.

Wrapping Up

Vertical AI Agents don’t start smart. They learn from data, get trained, and keep improving over time. But learning isn’t enough. AI needs tools to help it stay useful. From Retrieval-Augmented Generation (RAG) to APIs and workflow automation, these technologies help AI work more efficient, stay current, and improve over time. Improving enough to think for itself? Well, that’s a debate for another time.

Next time, we’ll dive deeper into how these tools work and how they help AI stay accurate, relevant, and powerful.

Introduction to Vertical AI Agents

By
Mark Freedman
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0

What is AI, and How Do Vertical AI Agents Fit In?

Artificial Intelligence (AI) is impacting up many industries, but not all AI works the same way. Some AI systems act like generalists — they can do a lot of different things but aren’t great at any one job. Jack of all trades; master at none. Others are trained specialists so they can excel in one specific area. These are Vertical AI Agents.

Just a quick note — I’m writing these articles as if the reader may not be familiar with several of these terms, or if they aren’t in the software field. Even if you have this experience, you may pick up something new.

AI vs. Regular Software

Typical software follows predefined instructions, like a cookbook where every recipe has step-by-step directions. But AI learns from experience by looking at patterns in data and improving its output over time.

For example, look at e-commerce recommendation engines like Amazon’s. Instead of following hard-coded rules, it studies what people browse and buy, like a store employee who learns what items to suggest based on what a customer has liked before.

Broad AI vs. Narrow AI – “Swiss Army Knife vs. Precision Tool”

AI generally falls into two categories:

  • General AI (the Swiss Army knife) can handle a variety of tasks but isn’t deeply specialized in one. Think about Siri, Alexa, or ChatGPT out-of-the-box. They can answer random questions but they aren’t experts in any one subject. But when models like ChatGPT are fine-tuned, they can become more focused, moving closer toward specialized AI.
  • Narrow AI (the precision tool) is built for a specific task. For example, an AI designed to detect fraudulent credit card transactions is focused on that task and wouldn’t be useful for answering trivia questions or writing blog post outlines.

What is a Vertical AI Agent? – “The AI That Masters a Single Industry”

A Vertical AI Agent is an AI system built to work in one specific industry (“vertical”). It’s designed to handle tasks within that domain better than a general AI could. For example:

  • Healthcare AI that assists doctors by analyzing medical images for early disease detection.
  • Finance AI that monitors transactions to detect fraud before it happens.
  • Music AI that helps sound engineers tweak levels for perfectly balanced tracks (something I’ve been using for my music).

AI agents don’t try to be everything to everyone. Rather, they focus on one field, like a developer who specializes in a single tech stack and eventually becomes an expert.

Other Info

AI Training and Model Optimization: AI models don’t start out smart. They need to be trained. The more relevant data they analyze, the better they get. It’s like debugging code — test, refine, reiterate, and optimize until it works correctly.

Why More Data Improves AI Accuracy: AI works best when trained with diverse, quality (and often high-volumes of) data. The more real examples it can learn from, the better its predictions become. Like as a developer writes more code, they get better at troubleshooting and problem-solving.

What’s Next?

Now that we know what Vertical AI Agents are, my next article will explain how they’re built. We’ll break down how they gather topic-specific data, train on real-world examples, and get deployed to solve specific problems.

Dropbox Cautionary Tale

By
Mark Freedman
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1

This is my first post in a long time. You know, pandemic, elections, world falling apart distractions. Will be prepping for my next AWS cert soon. But in the meantime, I have a cautionary tale about Dropbox.

I love Dropbox. It has saved me so much time. It’s the most reliable synchronizing software I’ve used. But beware of a fatal flaw I came across last week, which was almost a disaster.

I’m a backup fanatic. I have four copies of everything locally, and three more copies of everything in cloud services.

But I did not include my Dropbox folder in that set of backups. Because, you know, Dropbox.

On my 8-year-old MacBook Air (my traveling Mac machine), I have my Dropbox folder on an external drive. The internal drive is simply too small. If I restart the system without the drive connected, I properly get a simple warning, and I temporarily disable it. But last week the machine was already booted up, and the drive was connected. While moving things around to clean the room it was in, the drive got disconnected. I was unaware that this happened, so I did not yet reconnect it.

But Dropbox considered that to mean “delete all Dropbox files.”

All my files were disappearing on all the machines where I had Dropbox installed. I mean like five different machines. I noticed this when a development project I was working on started failing. I saw it was due to files disappearing. Panicked, I scrambled to turn off the automatic start setting of Dropbox on each machine and shut it down.

Too late.

But then I remembered I have the 30 day deletion recovery feature on Dropbox, so I desperately tried restoring. Over a million files. The site could not handle it. Spinning cursor. Spinning. Spinning. Canceling, I then tried restoring subfolders piecemeal. Spinning. Spinning. Error message.

I started digging through the site looking for a number to call to ask if I could get a physical hard drive sent to me with all my deleted files. I never found such an option (still don’t know if that’s available).

More panic.

Had lunch to take a breath. Then I realized, luckily, because the drive was never reconnected all the files were still on that drive! Saved! It took me several days, but now all my files are restored.

And now my Dropbox folder is part of my regular backup process.

I hope this helps others avoid this situation. We already have enough to worry about these days. Stay safe, everyone.

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