There's a lot of confusion right now about what an "AI agent" actually is. Most people hear the term and think of ChatGPT — a chatbot you type questions into and get answers back. That's not what I'm talking about. An AI agent is something fundamentally different, and it is quietly changing how real work gets done.

I build AI agents for businesses. Here's what that actually means, why it matters, and how it is different from the chatbot on your favorite website.

A Chatbot Answers Questions. An Agent Does Work.

A chatbot sits and waits for you to ask it something. It responds. That's it. It's reactive and conversational. Useful, sure — but limited.

An AI agent is proactive. It has goals, tools, and the ability to decide what to do next. You don't give it one question at a time. You give it an objective, and it figures out the steps to get there. It can read documents, query databases, call APIs, send emails, scrape websites, analyze data, and chain those actions together without you standing over its shoulder.

Think of the difference between asking someone a question and hiring someone to handle a project. That's the gap between a chatbot and an agent.

What This Looks Like in Practice

Let me give you some concrete examples from work I've done:

• Government procurement monitoring: An agent that scans federal procurement portals daily, identifies relevant solicitations based on your company's capabilities, extracts key details from PDFs, and sends you a prioritized morning briefing. No human touches it until the email lands.
• Document intelligence: An agent that ingests your company's entire library of contracts, policies, and technical manuals into a vector database. When someone asks a question — "What's our liability cap in the Acme contract?" — the agent searches semantically, finds the relevant clause, and answers with a citation. It doesn't guess. It retrieves.
• Lead generation and outreach: An agent that identifies potential customers by scraping public data sources, scores them against your ideal customer profile, drafts personalized outreach emails, and queues them for your review. You spend ten minutes approving instead of two hours researching.
• Data pipeline automation: An agent that monitors incoming data streams from IoT sensors, detects anomalies, correlates them against maintenance schedules, and creates work orders in your project management system. A human reviews the work order — not the raw data.

In every case, the agent handles the tedious, repetitive, research-heavy parts of the work. The human makes the final decision.

The Technology Behind It

Modern AI agents are built on a few key technologies working together:

• Large Language Models (LLMs) provide the reasoning engine. They understand natural language, can follow complex instructions, and generate coherent output. I work with Claude, OpenAI models, and open-source models like Llama depending on the use case and privacy requirements.
• RAG (Retrieval-Augmented Generation) grounds the agent in your actual data. Instead of relying on the model's training data (which might be stale or irrelevant), RAG systems search your documents in real time and feed the relevant context to the model. This is how agents give accurate, source-backed answers instead of hallucinations.
• Vector databases store your documents as mathematical embeddings — numerical representations that capture meaning, not just keywords. When the agent needs information, it performs a semantic search: "find me everything related to pump maintenance schedules" works even if no document uses those exact words.
• Tool use gives agents the ability to take action. An LLM by itself can only generate text. But when you give it tools — database queries, API calls, web scrapers, email senders, file readers — it becomes capable of interacting with the real world.

Why This Matters Now

Two things changed in the last year that made agents practical for small and mid-sized businesses:

1. Cost dropped dramatically. Running a capable AI agent used to require expensive API calls and significant infrastructure. Today, the models are faster, cheaper, and some can run locally on consumer hardware. I published research on split inference that lets you run large models on modest hardware while keeping your data private.
2. Reliability improved. Early agents were fragile — they'd get confused, loop endlessly, or produce nonsense. The current generation of models is significantly more reliable at following multi-step instructions, using tools correctly, and knowing when to ask for help versus forging ahead.

The result is that a custom AI agent — one built specifically for your workflows, your data, and your business rules — is now within reach for companies that don't have a Silicon Valley R&D budget.

Custom Agents vs. Off-the-Shelf AI Tools

You might be wondering: why not just use one of the many AI tools on the market? There are plenty of no-code AI platforms advertising "build your own agent in minutes."

Here's the honest answer: for simple use cases, those tools work fine. If you need a basic FAQ bot or a document summarizer, you don't need a custom build.

But if your workflow is specific to your industry, involves multiple data sources, requires integration with your existing systems, or handles sensitive data that can't leave your network — you need something built for you. Off-the-shelf tools hit a wall fast when the requirements get real.

I've been building custom software for 16 years across industries from oil and gas to healthcare to defense. The pattern is always the same: generic tools get you 70% of the way there, and the last 30% — the part that actually matters — requires someone who understands both the technology and the business problem.

Getting Started

If you're thinking about whether an AI agent could help your business, here's my advice: start with the task that eats the most time for the least value. The report nobody wants to compile. The data entry that runs three hours behind. The research that keeps someone from doing higher-value work. That's your first agent.

I offer free consultations and I'm happy to talk through whether an agent makes sense for your situation. No pressure, no pitch — just a conversation about what's possible. Reach out here or call me at 903-339-5048.