Recently I've been consulting quite a few teams about how to use AI in their companies. Within the last month, after several sessions, I realized that people are absolutely ignorant about what AI is capable of nowadays. They could be so much more productive.

So I put a little bit of thought into it and finally decided to start sharing my ideas. As a developer for 13 years, I want to explain in simple terms how AI agents actually work. When you understand the fundamentals, it helps you understand what this technology is capable of.

Here are the four basic components, explained in simple words:

(Note: These are the technological components that every agentic system consists of. I'm explaining them in a simple wayΓÇöthings can be more complex, but think of these as building blocks to understand what AI is capable of.)

The 4 Pieces of an AI Agent

Think of an AI agent like a set of building blocks. Here are the four essential pieces.

1. The Brain (The Reasoning Model)

Most leaders think of the Brain (or LLM) as a glorified chatbot. That's entry-level. The real power is its ability to process, structure, and create. Think of it as a universal data processor.

Important: This is the infrastructure that makes ChatGPT possibleΓÇönot ChatGPT itself. These are the underlying reasoning engines.

Who provides these brains? The main players are:

Anthropic (Claude 4 Opus & Sonnet): Best for complex problem-solving and following detailed instructions. Can work on tasks for hours without losing track. Sonnet is the budget-friendly option, Opus is the premium workhorse. Technical: 200K token context (~150K words), 72.5% on coding benchmarks, pricing $3-15/million tokens (Sonnet) or $15-75 (Opus).
Google (Gemini 2.5 Pro): The marathon runner - can process entire books or thousands of documents in one conversation. Excels at long, detailed discussions and can work with text, images, and video simultaneously. Technical: Industry-leading 1M token context (~750K words), Deep Think mode for complex reasoning, strong multimodal capabilities.
OpenAI (GPT-4.5 & o3-mini): The most popular choice, solid for general business tasks but expensive. Works well out of the box but can be tricky to integrate with existing systems. Professional verdict: overpriced for what you get. Technical: 128K token context, challenging API infrastructure, includes DALL-E 3 for image generation.
DeepSeek (R1 & V3): Chinese company claiming similar performance at dramatically lower costs. However, real-world testing hasn't consistently matched these claims - approach with skepticism. Technical: Claims 87.5% on math benchmarks, 671B parameters on V3, open source with MIT license. Alleged training cost of $5.6M vs competitors' $50-100M.
Meta (Llama 4): The "run it yourself" option - free software you can install on your own servers for complete data privacy. Requires technical expertise to set up. Technical: Most powerful open-source model, multimodal capabilities, requires significant infrastructure investment.

Beyond text processing: Many of these providers offer additional generative AI capabilities that can be chained together:

Image Generation: Create visuals from text descriptions (OpenAI's DALL-E, Google's Imagen, Stability AI's Stable Diffusion)
Video Generation: Generate video content from prompts (emerging capability)
Media Analysis ΓåÆ Generation Chain: Analyze existing media, then generate new variations or related content in a single workflow

How does data flow in and out? Think of it like ordering food through an app. You send your request (text, image, document) through an API connectionΓÇöbasically a digital messenger. The brain processes it and sends back the result. It's like having a conversation, but instead of talking, you're sending data packets back and forth.

It integrates with your existing systems: The real power isn't just generating text - it's creating data that automatically flows into your business tools. When you ask for an HR interview process, the AI doesn't just write a document. It creates structured information that your HR software can immediately understand and use - automatically populating forms, schedules, and workflows. Technical: Instead of returning plain text, AI generates structured data formats (JSON, XML, CSV) that existing software can read, enabling seamless integration into any data workflow where information is programmatically accessible.

The CEO's takeaway: This technology is the key component for making decisions. You feed it information as text, and you get back text that can be structured into complex data formats or kept simple. This can be used to reason, integrate, summarize, or collect research from all your existing business data. At the same time, it can be used for reasoning and solving your business problems.

2. The Memory (The Smart Database)

This is how an agent remembers your conversations, your company information, and what happened before. It's the secret to making your AI system actually smart about your business.

The human brain parallel: Your brain doesn't store memories like files in a cabinet. Instead, it creates connections between related concepts - when you smell coffee, you might remember your morning routine, a specific caf├⌐, or a conversation you had over coffee. Each memory is linked to many others through a web of associations. AI memory works remarkably similarly.

How ChatGPT remembers you: Every time you chat with ChatGPT or search/build your knowledge base with Perplexity, it's not just reading your words - it's creating a map of concepts and connections. When you mention "that project we discussed last week," ChatGPT doesn't search for those exact words. Instead, it finds related concepts through association - just like your brain connects coffee to mornings. This is why it can remember your preferences, your work style, and even anticipate what you might ask next. Technical: Conversations are converted to vector embeddings - numerical representations that capture meaning. These embeddings create a semantic map where related concepts cluster together, enabling associative retrieval through cosine similarity rather than keyword matching.

Building your company's brain: The same technology that helps ChatGPT remember you can create a living knowledge base for your business. Feed it your documents, emails, and data - they're transformed into this web of connections. Now when you ask "What are our best practices for client onboarding?" The system finds not just documents with those words, but related concepts: orientation processes, welcome emails, training materials, and first-week schedules. Your entire company knowledge becomes instantly accessible through natural questions. Technical: Documents are embedded into high-dimensional vector space, creating a searchable semantic index. This embedding layer allows retrieval augmented generation (RAG) - pulling relevant context into the AI's working memory without retraining the base model.

Here's how it works in practice:

Your company's documents are transformed into a format that captures meaning, not just keywords (Embeddings)
When you ask a question, the system finds conceptually related content, even if it uses different words (Semantic Search)
This relevant context is combined with your request to give the AI full understanding (Retrieval Augmented Generation)
The system learns from every interaction, getting better at understanding your specific needs (Self-Improvement)

The CEO's takeaway: Modern AI doesn't just store information - it understands relationships between concepts. This memory layer is what makes AI feel personalized and get smarter over time, without the massive cost of retraining the entire model.

3. The Tools

An AI that only thinks and remembers is a consultant. To get real work done, it needs Tools. Tools are what allow an agent to stop talking and start doingΓÇösending an email, updating your CRM, or booking a meeting.

A chatbot might say: "You should email the client to confirm their order."

An agent says: "I've drafted a confirmation email, and it's in your drafts folder awaiting your approval. Or, I can just send it now. Your call."

How agents decide which tool to use: Imagine training a new assistant. You give them access to your email system with five capabilities: read emails, draft emails, send emails, search emails, and schedule emails. Then you explain the rules: "When a client asks about order status, first search for their previous orders, then draft a status update, and put it in my review folder." The agent follows these instructions exactly - except it never forgets, never gets tired, and processes hundreds of requests simultaneously.

Programming in plain English: Here's how you might configure an agent to handle customer inquiries:

"When someone asks about pricing, search our price database"
"If they're an existing customer, apply their discount tier"
"Draft a personalized quote with their company name"
"If the amount is over $10,000, flag for human review"
"Otherwise, send automatically with our standard terms"

This is the new programming - no code, just clear instructions like you'd give a smart assistant. The critical skill isn't coding anymore; it's breaking down processes into logical steps. Technical: Tools are exposed through function definitions with parameters and descriptions. The LLM uses these descriptions to determine tool selection through semantic matching. Execution follows a reasoning chain where the model evaluates context, available tools, and constraints to select appropriate actions.

The CEO's takeaway: Any task that can be described and is performed through digital systems can be automated by an AI agent. Give these automation tools to every team member - not to replace them, but to multiply their impact. They'll spend less time on repetitive work and more time on creative problem-solving. One person monitoring automated workflows can accomplish what previously took a team. The key: if you can write instructions for a human to do it online, an agent can do it automatically.

4. The Plan (System Instructions)

The Plan is the logic that governs the agent's behavior. It's what separates an AI that can handle one simple command from an AI that can manage a multi-step workflow.

Step-by-Step Processing (Assembly Line): Think of a factory assembly line. Each worker does exactly one task - insert screw A into hole B, attach part C, pass to next station. No decisions, no adaptation, just rigid execution. This works perfectly for predictable, repetitive tasks like data entry, report generation, or invoice processing. The agent follows a strict script: Step 1, then Step 2, then Step 3. If something unexpected happens, it stops and waits for human help.

Autonomous Agent Framework (Smart Assistant): This is like having a skilled executive assistant who understands your goals and can figure out how to achieve them. You say "prepare for tomorrow's board meeting" and they know to check attendees, gather reports, prepare slides, book the room, and order catering - adapting their approach based on what's needed. These agents make decisions, learn from your feedback, and improve over time. They handle the unexpected and find creative solutions.

Both have their place: Use step-by-step for compliance-critical processes (financial reporting, regulatory filings). Use autonomous agents for dynamic work (customer service, research, project management).

The CEO's takeaway: The biggest economic impact will come from autonomous AI agent frameworks. Every business needs to transform into an AI-assisted company where humans oversee while agents execute. This isn't about replacing people - it's about automating every automatable process in your company so your team can focus on strategy and innovation. This transformation is exactly what forward-thinking companies are implementing right now to stay competitive.

Your Action Plan

Understanding these terms isn't academicΓÇöit's the foundation for transforming your business. Here's your challenge:

Step 1: Analyze Your Daily Work (This Week)

Track everything you do for one day - every email, every report, every task
Or shadow a colleague and document their repetitive work
Mark each task: Could an AI do this? Yes/Maybe/No

Step 2: Pick Your First Automation Target

Look for tasks that are:

Repetitive (done daily/weekly)
Digital (happens on a computer)
Rule-based (follows a clear process)
Time-consuming (takes 30+ minutes)

Step 3: Reverse Engineer the Process

Break it down like training a new hire:

What information is needed?
What decisions are made?
What actions are taken?
What's the output?

Your Turn: Comment below with the #1 manual process you're still doing daily that drives you crazy. Is it email management? Report generation? Data entry? Customer responses? Share it, and I'll help you figure out how to automate it with AI. Your question might become my next video tutorial.

Remember: If you're paying someone to do repetitive digital work, or spending hours on it yourself, you're leaving money on the table. Every manual process is an opportunity for AI automation.

Understanding AI Agents: The 4 Essential Components Every Business Leader Should Know

Ready to Transform Your Business with AI?