AI Agents Explained: Complete Guide to Building Your First Agent

Everyone's talking about AI agents being the future of business. But if you're like most people, you're hearing all the hype while secretly thinking, "What the hell actually is an AI agent?" This comprehensive guide is made for people like myself - you're not super technical, but you use AI tools like ChatGPT regularly, and you want to learn just enough about AI agents to see how they affect you and how you can start taking advantage.

I'm breaking this down into three different levels, building on concepts you already understand, like ChatGPT, then moving on to AI automations, and finally AI agents. At the end, I'll share what a Y Combinator partner recently said about AI agents that completely changes the way I think about business opportunities in 2025.

Level 1: Large Language Models (LLMs)

The most popular examples are ChatGPT, Claude, and Gemini. If you're reading this, you've probably already used one of these tools for writing, summarizing, or brainstorming ideas. These models have been trained on massive amounts of data, so they can generate human-like text on pretty much any topic you can think of.

For example, if you ask ChatGPT to write a welcome email for new customers, it delivers exactly what you asked for - professional, personalized, and ready to use. But watch what happens when you ask, "What meetings do I have on my calendar today?" It gets completely stuck.

This highlights two key limitations of basic LLMs:

Limited knowledge of proprietary information like personal data or internal company information
Passive nature - they wait for a prompt and then respond

Think of an LLM like your favorite barista. They're incredibly skilled and can make any drink you want, from a basic latte to a caramel macchiato with oat milk and custom foam design. But they're just going to stand there waiting until you walk up and actually place your order.

To be fair, this is changing. OpenAI recently released their connectors feature, and Claude has similar capabilities. You can now connect ChatGPT directly to your Google Calendar, Gmail, and other tools. This is definitely a step forward, but it's still completely reactive - it won't proactively check your calendar, analyze your emails, or take any actions unless you specifically prompt it to.

Level 2: AI Automations

This is where we move from reactive conversations to proactive workflows. AI automations work on a simple principle: if this happens, then do that. You have a trigger (something that sets the process in motion) and a series of actions that take place automatically in a predefined sequence.

Building an Email Management Automation

Let me show you exactly what this looks like with an automation I built to manage my Gmail inbox using n8n (a powerful automation platform you can sign up for free).

Here's how this automation works:

Gmail Trigger: Whenever I receive a new email to a specific inbox, it triggers the automation
Email Classification: ChatGPT receives the email contents and categorizes it based on:

Category (one of 10 different options)
Level of urgency
Whether the message needs a response

Routing: Based on ChatGPT's analysis, the email gets routed through different branches:

Urgent branch: Assigns labels, creates drafts, sends Slack notifications
High/Medium/Low priority branches: Different actions for each priority level
Low priority: May archive emails or move them out of the inbox

This automation is way more capable than a basic LLM, but it still has limitations. It's somewhat rigid in the predefined sequence it follows - like a line cook following a recipe to the letter.

Limitations of AI Automations

What happens when the situation changes? What if:

The email is from your biggest client and needs special handling?
It's part of an ongoing conversation and needs additional context?
Your calendar availability matters for your response?

That's where traditional automations start to break down, as they can't adapt to unique situations outside their predetermined paths.

Level 3: AI Agents

This is where the AI doesn't just follow instructions but actually becomes the decision maker. AI agents can reason, act, and iterate. In the AI world, we call this the ReAct framework - they can think, choose the right tools, take action, and then refine their approach until they get the result you're looking for.

If AI automations are like a line cook following a set recipe, AI agents are like the executive chef. They design the menu, source the best ingredients, assign tasks to the kitchen team, and make sure every plate meets the highest standard before it leaves the kitchen.

Building an AI Email Agent

Here's an example of an AI agent I built in n8n that does basically the same thing as the previous automation example, but with much more intelligence and flexibility.

One thing you'll notice immediately is that relative to the AI automation flow, the setup of the AI agent is much more streamlined. Rather than defining every single path each scenario might take, we give the AI agent general instructions about the goal or outcome, provide it with the tools it needs, and let it think and reason on its own.

Key Components of an AI Agent

1. Trigger: Still starts with Gmail receiving a new email

2. Chat Model (The Brain): Connected to a specific AI model like:

ChatGPT models
Anthropic's Claude
Any other LLM can serve as the brain

3. Memory: More relevant for chatbot use cases, but we use a form of memory to access conversation history

4. Tools: The specific actions we give the AI agent access to:

Grab different labels from Gmail
Add/remove labels from emails
Archive emails
Send Slack notifications
Mark messages as read
Create email drafts
Check calendar events
Get message history for context

5. Output: Where the agent sends results (Slack, email, text, etc.)

The System Message: Your AI Agent's Job Description

Within the AI agent node, the system message is like the job description for your AI agent. You're giving it the role and telling it what its task will be and detailed instructions on how to complete that task.

For example: "You are an intelligent email management agent that analyzes, plans, and executes comprehensive email management tasks."

The system message includes:

Rules and capabilities
Available tools and execution order
Labeling instructions
Notification procedures
Error handling protocols

The user message is the specific task for that particular instance - the individual email you want the agent to process with relevant information.

AI Agent in Action: Live Example

Here's how the AI agent handles a real email:

Email received: "SOS, my CRM looks like it's been hacked by a raccoon" from Mark

The AI agent:

Analyzes the email content and urgency
Gets past messages from that sender for context
Creates a draft response: "Hi Mark, I'm on it. This definitely sounds like an important issue..."
Assigns appropriate labels: Business category, Urgent priority
Sends Slack notification with summary of actions taken
Provides detailed output: "Email processed, category: business, priority: urgent, professional response drafted, urgent Slack notification sent, processing complete."

The key difference? The AI agent reasoned through the situation, understood the urgency, accessed relevant context, and took appropriate actions without following a rigid predetermined path.

Beyond Email Management

This level of intelligence can be applied to numerous use cases:

Customer support: Intelligent ticket routing and response drafting
Content creation: Research, writing, and optimization workflows
Sales: Lead qualification and follow-up automation
Marketing: Campaign optimization and audience targeting
Project management: Task prioritization and resource allocation

The Y Combinator Opportunity That Changes Everything

Here's the insight that completely shifted my perspective on AI agents and business opportunities in 2025:

One of the managing partners at Y Combinator (the startup accelerator that funded Airbnb, Stripe, and Dropbox) recently said that for every SaaS company, there will be a corresponding AI agent company.

Think about that for a second. AI agents aren't just a productivity tool - they're the foundation of entirely new businesses. Just like SaaS reshaped entire industries over the past two decades, AI agents are going to create a wave of new companies that solve specific problems completely autonomously.

The Business Opportunity

This represents a massive shift in how we think about business models:

SaaS companies provide software tools that humans use
AI agent companies provide autonomous systems that work independently
Every industry will have opportunities for AI agent solutions
First movers will have significant advantages in establishing market position

We're at the beginning of a new wave of innovation that could be as transformative as the rise of SaaS companies themselves.

Getting Started with Your First AI Agent

If you want to take advantage of this opportunity and build your first AI agent, the best approach is to start with a specific problem you face regularly. Email management is an excellent starting point because:

It's a universal business need
The workflow is well-defined
The impact is immediately measurable
It demonstrates core AI agent capabilities

Key Steps to Build Your First AI Agent:

Identify a repetitive task you handle manually
Map out the decision-making process you currently follow
Choose your tools (n8n is excellent for beginners)
Define the agent's role and capabilities
Connect necessary tools and data sources
Test and iterate based on real-world performance

Tools and Resources

To get started building AI agents:

n8n: Powerful automation platform with AI agent capabilities
Make (formerly Zapier): User-friendly automation with AI features
LangChain: For more technical implementations
OpenAI API: For custom AI agent development

The Future of AI Agents

As AI agents become more sophisticated, we'll see them handling increasingly complex tasks:

Multi-step reasoning across different business functions
Learning and adaptation from past interactions
Collaboration between multiple specialized agents
Integration with existing business systems and workflows

The companies that start experimenting with AI agents now will be best positioned to capitalize on this transformation as the technology matures.

Conclusion: Your Next Steps

AI agents represent a fundamental shift from reactive AI tools to proactive, intelligent systems that can reason, act, and iterate. While we started with simple LLMs like ChatGPT, the evolution to AI automations and finally AI agents opens up entirely new possibilities for business automation and innovation.

The Y Combinator insight about AI agent companies corresponding to every SaaS company isn't just interesting - it's a roadmap for the next decade of business opportunities. Whether you're looking to improve your personal productivity or build the next breakthrough business, understanding and implementing AI agents is no longer optional.

Start with a simple use case like email management, learn the fundamentals, and then expand to more complex applications. The future belongs to those who can effectively leverage AI agents to solve real problems autonomously.

What would you have your first AI agent do for you? The possibilities are limitless, and the time to start experimenting is now.