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Almost Timely News: 🗞️ How to Connect an AI Agent to a Data Source (2026-07-19) :: View in Browser
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What’s On My Mind: How to Connect an AI Agent to a Data Source
This week, a direct request from my reader survey, how to connect a data source to an AI agent. Reader requests are how I know what you want to read, so if you haven’t filled out the survey, although the prize drawing has ended, you can still potentially win something - an answer to your question.
So let’s dig in with a bit of table setting first.
Part 1: What is an AI Agent?
I’ve written about this a bunch of times but it bears repeating: an agent is a piece of software that does something without you and returns its results to you.
A real estate agent helps you buy or sell your home but does a lot of work without you, once they know your requirements. A travel agent helps you plan a vacation. An insurance agent sets you up with a policy. A Wifi negotiation agent on your phone connects you to a wifi network.
Agents do stuff without you. Here’s how you know if you’re using an agent or not - after an agent has started a task, if you are still providing input, feedback, and steering throughout the process, it’s not an agent.
Imagine a personal chef who was asking you at every step of the way how you wanted a step of the recipe cooked. That’s not an agent; an assistant, yes, an agent, no.
If you give a personal chef a solid recipe, ingredients, a good kitchen to work in, and your preferences, the next thing that should happen is a plate of your food appearing in front of you. That’s an agent.
In the AI Maturity Framework by Trust Insights, I detail 5 levels of AI:
Level 1, done by you: You’re doing most of the work. These are tools like ChatGPT and its kind, chat assistants where you’re doing the work, copy pasting, etc.
Level 2, done with you: You’re doing half the work. These are tools like GPTs, Gems, Claude Projects, etc. Where you’ve got a standard operating procedure in place but you’re still handling a lot of the outputs and conversational turns.
Level 3, done for you: You’re doing little of the work. These are tools like Google Antigravity and Gemini Spark, ChatGPT Work, Claude Cowork and Claude Code, OpenCode, etc. You give them project plans and all the necessary pieces up front, and they do the work. This is the first truly agentic level.
Level 4, done without you: You’re doing almost none of the work. These are tools like OpenClaw and Hermes Agent that run nearly autonomously. Once you’ve used a Level 3 tool to set them up, they do their thing, they self-learn and self improve.
Level 5, done ahead of you: Agents proactively working on your behalf. There aren’t any production-level systems like this yet, but they have persistent memory and self reflection, enough to know that if you’ve asked for a monthly report for the last 3 months, it’s probably a safe bet you’ll need a monthly report this month and they proactively do it for you.
When we talk about connecting agents to data sources, we are talking Level 3 and up.
Part 2: What is a Data Source?
Okay, this may seem silly, but it matters. When we say connect an agent to a data source, what do we mean by a data source? Remember that fundamentally there are two classes of data source, structured and unstructured.
Structured data fits in tables and spreadsheets neatly, and is usable in those tools.
Unstructured data doesn’t.
For example, let’s say you have some customer satisfaction data. Some of it will be structured, like what rating someone gave you on a scale of 1 to 10. A spreadsheet filled with numbers is very clearly structured data because you can work with it in that environment.
Some of it will be unstructured, like the free form responses people give. “I used your Turbo Ultra Vacuum to try vacuuming up the leaves in my 200 acre forested yard and it didn’t work, your product sucks, I want a refund” would be unstructured data.
Knowing what kind of data you want to connect to an agent matters a lot because it will dictate how we get the data to the agent, and what the agent will need to do with it.
The second thing that’s important to know is the difference between deterministic and probabilistic, fancy terms for “things that have no random chance” and “things that have random chance”.
In general (and yes, there is nuance and variation), structured data tends to be deterministic which means you need non-AI tools to work with it. It often requires math, and AI isn’t great at math.
In general, unstructured data tends to be probabilistic and ideal for AI to work with. AI can think through what a comment means, what topics or subjects it’s about, and even determine things like sentiment or emotional intensity.
Beyond the kind of data you have, the even bigger question is how the data is available. I assume if the goal is to have an agent working with your data, then you’d probably not want to babysit the agent by hand feeding it data. Hand feeding animals is fun. Hand feeding agents is not.
That means the data has to be available in some automated fashion, which can mean:
An API (application programming interface), a dedicated computer interface that serves up data, requires you to have AI write code to talk to it
An MCP (model context protocol), an AI-specific data connector that’s very resource intensive but requires no additional code
A web page, which means AI has to use a browser or browser-like code to scrape the data and extract it
In general, an API is your best bet for data because AI is very good at writing code, and when you use code to extract data, you incur no AI usage. One of our overall goals is to only use AI for things it’s good at, so having regular code (deterministic) to extract data is the way to go.