What Are AI Agents?

A Simple Way to Think About AI Agents

Picture a world class chef in a kitchen. Not someone staring at a recipe, but someone who knows what’s in the fridge, understands what you want, and adjusts in real time to deliver the dish.

They don’t just recommend. They execute. They check progress, correct mistakes, and finish the job.

That’s the difference between a language model and an AI agent.

A model can talk about the work.

An agent can do the work.

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From Language Models to Agents

Most people now recognize tools like ChatGPT, Gemini, and Claude.

They’re excellent for:

• answering questions
• summarizing content
• drafting writing
• helping with code

But on their own, they have limits. They do not automatically pull fresh data. They do not log into systems. They do not run workflows. They do not keep working on a task unless you keep prompting them.

Agents change that. An agent pairs a model with a set of capabilities that let it:

• gather information from tools and sources
• decide what to do next
• run steps in sequence
• retry when something fails
• stop when the result is complete
  

Where a model responds, an agent operates.

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What Makes an Agent Work

The reasoning engine

This is the model. It plans steps, keeps context, and makes decisions. Instead of answering one question, it works through a process.

Tools

Tools are how the agent interacts with the real world. That can mean APIs, databases, search, document parsing, or internal systems.

For Mosaic Theory use cases, tools often include:

• pulling ownership and entity data
• querying county property records and filings
• scanning documents for names, relationships, and transactions
• extracting product, client, and partnership signals from websites and PDFs

Tools turn “I think” into “I checked.”

Orchestration

This is the control layer that runs the loop:

• choose the next step
• select the right tool
• evaluate the output
• decide whether to continue or stop

It is how agents handle multi step work without falling apart.

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Why Agents Matter at Mosaic Theory

We build data products that reflect the real world as it changes. That means keeping up with messy, fragmented sources that update constantly.

Agents help us do this at scale by collecting, structuring, and verifying signals across documents, registries, and filings.

Here are a few examples of what that enables.

Resolve property ownership

Public records are scattered across counties and jurisdictions. Agents can parse documents, extract entities, and tie assets back to the right people and companies with a clear audit trail.

Track company activity

Agents monitor entity changes, registrations, and filings across registries to keep a living view of private market activity.

Surface product and client signals

Agents extract mentions of products, partnerships, and customer segments from sources like PDFs, websites, and disclosures, then structure the results so they can be searched and compared.

Connect the dots over time

Agents link records across sources and time periods, reducing manual clean up and increasing confidence in the output.

Agents don’t replace judgment. They reduce the busywork and make the data more current, more structured, and more defensible.

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How Agents Plug Into Real Systems

Extensions

Direct tool access, like calling an API, querying a database, or running a search.

Functions

Structured outputs that your application executes. This keeps control in your backend while still using the agent to generate clean inputs.

Data stores

A way to ground the agent in your documents and internal data. This is where techniques like Retrieval Augmented Generation (RAG) matter, because the output is tied back to source material.

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The Bottom Line

Agents are not chatbots. They are systems built to finish tasks.

They can:

• work through multiple steps
• pull and verify real world data
• use tools to take action
• adapt as new information appears

If your job depends on turning scattered public information into decision grade market intelligence, agents are how you do it faster, with more coverage, and with fewer gaps.

If you want to see what this looks like on your use case, contact us.