AI agent. You’re right to ask yourself what that even is—it’s one of the hottest topics in the tech startup space right now. It seems like YC only accepts AI agentic projects these days, and honestly, there’s a good reason for that. By the end of this blog post, you’ll see why.
You’ll understand what an agent is, how it’s different from an LLM or ChatGPT, how they work, what real-life implications AI agents have, and why this AI agent market is absolutely massive.
So let’s get in…
LLMs (large language models, e.g., GPT-4 or Claude 3) have access only to the data and knowledge they were trained on, and in this sense, they’re limited. They’re great at tasks like summarizing large amounts of data, drafting emails, or helping you think through and reason about general information.
But here’s where they’re limited:
LLMs lack proprietary data—meaning, they lack the specific data and context you need to make more personalized, accurate decisions. For example, if you want to plan a summer vacation and need to know how many days you can take off work, ChatGPT can't help you. It doesn’t know who you are, where you work, or when your colleagues have scheduled their vacations.
So, while a raw LLM chatbot is useful for the above-mentioned tasks, the real magic is unlocked when you start building systems around the model and integrating it into your existing processes.
What does this mean?
In the specific vacation planning example, you’d need to connect the LLM to a database that holds specific information—like your schedule, your coworkers’ schedules, company deadlines, etc.—and use that data to provide you with a personalized answer.
In this case, when you ask the LLM to plan your vacation, it could transform your query into a database search, gather relevant information, reason over it, and return a meaningful answer. This is an example of an AI compound system—a network of connected components like databases, tools, and different AI models that allow a more complex task to be completed, which a stand-alone LLM couldn’t manage.
You connect all the components using programmed logic.
But this is not an AI agent just yet…
The compound AI system becomes an AI agent when the LLM is put in charge of the system. You prompt the LLM by giving it an objective, some rules on how to achieve it, possible exceptions to account for, and guidance on what to do in certain scenarios (like asking for help if it gets stuck). Within this prompt, the LLM acts as the agent—reasoning, coordinating, and moving between different modules, tools, and systems.
So essentially, an AI compound system is a system of modules (tools, databases, LLM models, software, etc.) connected by a logic—like a program or an automation of some kind. In this case, the LLM is only included as one of the modules that the system uses, while in the case of a real AI agent, the connection between the modules is not just a logic, but a trained LLM with context about your case that oversees the whole system and manages the steps—like a person would.
An AI agent can:
- Reason – Pursue goals with given instructions and data instead of just following fixed logic.
- Act – Use tools, search engines, databases, or even other agents.
- Access memory – Remember past conversations, feedback, or outcomes and improve over time.
So how does an AI agentic workflow look like?
It starts with a user query. This could be a question typed into a chatbot interface. Instead of rushing to answer (like a chatbot such as ChatGPT would), the agent follows your prompt instructions. For example, you might prompt the agent beforehand:
Don’t answer right away. First, plan how you’ll approach the task. Then use your tools and data sources. Afterward, reason through the results and check whether your answer fits the criteria. Only then return it—otherwise, try again.
Let’s return to our vacation planning example. Say you ask: "How much sunscreen should I take to Florida?" This isn’t a simple question. The agent might need to:
- Look up the weather forecast for Florida.
- Cross-check your vacation dates using the company’s HR system.
- Recall from memory that you said you'd be going to Florida in July.
- Access general medical guidelines for sunscreen usage.
It’s a complex process—but once all tools and data sources are connected and the LLM is properly prompted, the agent can coordinate the steps and provide a tailored answer you’d never get from a standalone chatbot.
How AI Agents Can Help Companies Become More Efficient
In short?
Virtually every repetitive business task that doesn’t require original thinking or high-level strategy can be automated with an AI agent.
Think about:
- Updating systems with new data
- Extracting and transferring info between tools
- Organizing, summarizing, or matching data (e.g., generating performance reports)
- Invoice and document matching
- Customer support questions
Your imagination is the limit. If it’s repetitive, digital, and done by a person using a computer—there’s likely an agent for that waiting to be developed and deployed.
But here is where it gets really interesting…
Why the AI Agent Market Is Absolutely Massive:
Here’s why this is bigger than SaaS:
While SaaS tools revolutionized workflows, they often introduced new manual work—someone still has to operate them. Millions of people are paid to click through UIs, transferring information, checking values, or doing low-value tasks that must be done.
AI agents will replace that work.
Even better, they’ll perform the same work at a marginal cost compared to what it would cost to employ humans for the same tasks.
Imagine replacing one employee who costs $70k/year. Now imagine replacing 10 or 20. That’s a massive business efficiency gain. And it doesn’t scale like SaaS, where growth requires more hires. With agents, growth means more workflows automated—not more people needed.
This is why the AI agent space is an exciting blue ocean, full of opportunities. If you’ve got a vision and need a partner to bring it to life, you can click here to discuss it—free of charge—with our CEO.
