AI Agents Explained in 10 Minutes: The 7 Core Building Blocks

If you're a developer, keeping up with the relentless pace of the AI space can feel nearly impossible. Your social media feeds are saturated with discussions about AI agents, with everyone making it seem deceptively simple. Yet, you might still be wrestling with fundamental choices like LangChain versus Llama Index, all while trying to debug the AI agent systems you're tinkering with.

The available tutorials often feel messy or contradictory, and every week, a new, popular article drops, leaving you wondering if there's yet another technology you need to master. It's a state of organized chaos.

The goal of this article is to cut through the noise, calm your AI anxiety, and provide clarity on what truly matters in the AI landscape. You can safely ignore 99% of the online chatter and concentrate on the foundational building blocks for creating reliable and effective AI agents.

This article will walk you through the seven foundational building blocks essential for building AI agents, regardless of your chosen tools or programming language. The code examples are provided in Python, but the principles are universal and can be implemented in TypeScript, Java, or any other language. By boiling it down to these core components, you'll see how simple they are.

We will explore these simple code blocks, examine their output, and walk through everything step-by-step with conceptual explanations. Even if you've never written a single line of Python, you can follow along. After reading this article, you'll have a completely different perspective on what it takes to build effective AI agents. You'll be equipped to look at almost any problem, break it down, and identify the patterns and building blocks needed to automate a solution.

The Illusion of Simplicity in AI

The primary reason for the widespread confusion among developers stems from a simple market reality: a massive amount of money is flowing into the AI industry. Historically, whenever such an opportunity arises, people rush to capitalize on it.

This gold rush has led to social media feeds filled with tools that promise to build entire agent armies with ease. Yet, many developers are still left wondering where to begin to make it all work in a production-ready environment. This trend is mirrored by a proliferation of frameworks, libraries, and developer tools that also market an overly simplified path to building AI agents. Combined with constant news cycles, the result is an overwhelming sense of confusion and a lack of focus.

The Smart Developer's Approach

A clear distinction exists between the top developers and teams who successfully ship production-level AI systems and those who remain stuck debugging the latest agent frameworks. Most developers chase the hype—social media trends, new frameworks, and the endless stream of AI tools.

In contrast, smart developers understand that nearly everything you see is merely an abstraction built on top of the current industry leaders: the LLM model providers. Once you, as a developer, begin working directly with these providers' APIs, you realize that you can ignore 99% of the online noise.

Fundamentally, very little has changed since the introduction of function calling. Yes, models are improving, but the core methods for interacting with LLMs remain the same. Codebases from two years ago still function perfectly; the only necessary change is updating the model endpoints via the APIs. This resilience comes from engineering systems that are not dependent on frameworks built on what is essentially quicksand.

Why Custom Building Blocks Trump Frameworks

The most effective AI agents are not as "agentic" as they might seem. They are primarily deterministic software systems with strategic LLM calls placed precisely where they deliver the most value.

The issue with most agent frameworks and tutorials is their tendency to give an LLM a bundle of tools and let it "figure out" how to solve a problem. In reality, you don't want your LLM making every decision. You want it to handle what it excels at—reasoning with context—while your application's code handles everything else.

The solution is straightforward software engineering. Instead of making a single LLM API call with over a dozen tools, you should tactfully break down your objective into fundamental components. Solve each sub-problem using proper software engineering best practices, and only introduce an LLM step when it's impossible to solve with deterministic code.

An LLM API call is currently the most expensive and unpredictable operation in software engineering. While incredibly powerful, it should be used sparingly, especially in background automation systems.

A Note on System Types: Personal Assistants vs. Backend Automation

It's crucial to understand the difference between building personal assistants (like ChatGPT or Cursor), where a user is always in the loop, and creating fully automated systems that process information or handle workflows without human intervention.

Most developers are not building the next conversational AI. Instead, they are creating backend automations to enhance efficiency. For personal assistant applications, using multiple tools and LLM calls can be effective. However, for background automation, it's vital to minimize these calls. In production environments for clients, for instance, tool calls are almost never relied upon. Build your applications to require as few LLM API calls as possible. Only when a problem cannot be solved with deterministic code should you make that call.

The Power of Context Engineering

When you do need to call an LLM, success hinges on context engineering. To get a high-quality response from an LLM, you must provide the right context, at the right time, to the right model. This involves pre-processing all available information, prompts, and user inputs so the LLM can solve the problem easily and reliably. This is the most fundamental skill in working with LLMs.

Finally, remember that most AI agents are simply workflows (or DAGs, to be precise). The majority of steps in these workflows should be standard code, not LLM calls. This article aims to help you understand AI agents from first principles.

The 7 Foundational Building Blocks of AI Agents

With the stage set, let's dive into the foundational building blocks. There are just over six core components you need to deconstruct any problem and solve its constituent parts.

1. The Intelligence Layer

This is the only true "AI" component in your system. It's where the magic happens—the actual API call to the large language model. Without this, you simply have regular software. The challenge isn't the LLM call itself, which is straightforward, but everything you need to build around it.

The basic pattern is simple: a user provides input, you send it to the LLM, and the LLM returns a response.

Here is a simple example in Python using the OpenAI SDK:

from openai import OpenAI

# Connect with the client
client = OpenAI()

# Select the model and provide a prompt
response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {"role": "user", "content": "Tell me a joke about programming."}
  ]
)

# Get the response
print(response.choices[0].message.content)

This is the first foundational block: a way to communicate with an LLM and receive information.

2. The Memory Block

This block ensures context persists across interactions. LLMs are stateless; they don't remember previous messages. Without memory, every interaction starts from scratch. You must manually pass the conversation history with each new request. This is analogous to storing and passing conversation state, a concept long-established in web development.

To build on the intelligence layer, you now provide not just the user's prompt but also the previous context, structured as a sequence of messages. Your application must also handle updating the conversation history.

Consider this example where we first ask a question, then ask a follow-up without providing context:

# First, we ask a joke without saving history
# The LLM will respond with a joke.

# Then, we ask a follow-up question without context
# "What was my previous question?"
# The LLM will respond: "I'm unable to recall previous interactions."

Because LLMs are stateless, the model has no memory of the first question. Here is the proper way to handle memory, where we pass the conversation history:

# In a real application, this would be stored in a database
conversation_history = [
    {"role": "user", "content": "Why do programmers prefer dark mode?"},
    {"role": "assistant", "content": "Because light attracts bugs."}
]

# Add the new question to the history
conversation_history.append(
    {"role": "user", "content": "What was my previous question?"}
)

# Make the API call with the full history
response = client.chat.completions.create(
  model="gpt-4o",
  messages=conversation_history
)

# The LLM will now correctly answer:
# "Your previous question was asking for a joke about programming."

3. Tools for External System Integration

Often, you need your LLM to do things, not just chat. Pure text generation is limited. You want to call APIs, update databases, or read files. Tools allow your LLM to signal its intent, like, "I need to call this function with these parameters," and your code handles the execution.

The process involves augmenting the intelligence layer with a set of available tools. For each API call, the LLM decides whether to use one or more tools. - If no, it provides a direct text answer. - If yes, it selects a tool. Your code is responsible for catching this, executing the tool, and passing the result back to the LLM to formulate a final response.

Tool calling is directly supported by all major model providers, so no external frameworks are needed.

# 1. Define the function you want the LLM to be able to call.
def get_weather(location):
    """Gets the current weather for a specified location."""
    # In a real scenario, this would call a weather API.
    return f"The weather in {location} is sunny."

# 2. Create a tool schema for the LLM.
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                },
                "required": ["location"],
            },
        },
    }
]

# 3. Your code checks if the LLM decided to call the tool and executes it.
# This logic would be part of your application flow.
# The LLM can now use the get_weather function to answer questions
# about the weather for any city.

Through tools, we give the model a way to integrate and connect with external systems, extending its capabilities beyond text generation.

4. The Validation Block

This block is for quality assurance and structured data enforcement. To build effective applications, you need to ensure the LLM returns JSON that matches your expected schema. LLMs are probabilistic and can produce inconsistent outputs.

You validate the JSON output against a predefined structure. If validation fails, you can send it back to the LLM with the error and ask it to fix it. This concept is known as structured output, and it's crucial for engineering reliable systems.

Instead of just getting text back, you want a predefined JSON schema, ensuring you receive the fields your application can use. The process is as follows: 1. Ask the LLM for structured output (JSON). 2. Validate it against a schema using a library like Pydantic. 3. If valid, you have your structured data. 4. If invalid, send the error back to the LLM for correction.

For example, to build a task management tool that processes natural language, you can define a specific data structure:

from pydantic import BaseModel

# Define the data structure with Pydantic
class TaskResult(BaseModel):
    task: str
    priority: str

# Request structured output from the LLM
response = client.chat.completions.create(
  model="gpt-4o",
  messages=[
      {"role": "system", "content": "Extract task information from the user input."},
      {"role": "user", "content": "I need to complete the project presentation by Friday, it's high priority."}
  ],
  response_format={"type": "json_object", "schema": TaskResult.model_json_schema()}
)

# You get a validated data object back that you can use programmatically.
# result = TaskResult.model_validate_json(response.choices[0].message.content)
# print(result.task) -> "complete the project presentation by Friday"
# print(result.priority) -> "high"

Using libraries like Pydantic is at the core of context engineering, allowing you to validate both incoming and outgoing data.

5. The Control Block

This block is for deterministic decision-making and process flow. You don't want your LLM making every decision. Some things are better handled by regular code. Use if/else statements, switch cases, and routing logic to direct the flow based on specific conditions. This is standard business logic.

For example, you can use an LLM to classify the intent of an incoming message and then use simple if statements to route the request.

from pydantic import BaseModel
from typing import Literal

# Define a data model for intent classification
class Intent(BaseModel):
    intent: Literal['question', 'request', 'complaint']
    confidence: float
    reasoning: str

# A function to classify intent using the LLM
def classify_intent(text: str) -> Intent:
    # This function would call the LLM with the text and the Intent schema
    # to get a structured response.
    pass

# Use simple if/else statements to route based on intent
def handle_message(message: str):
    classification = classify_intent(message)

    if classification.intent == 'question':
        handle_question(message)
    elif classification.intent == 'request':
        handle_request(message)
    elif classification.intent == 'complaint':
        handle_complaint(message)

# Example usage:
# handle_message("What is machine learning?") -> routes to handle_question
# handle_message("Please schedule a meeting for tomorrow.") -> routes to handle_request
# handle_message("I'm unhappy with the service quality.") -> routes to handle_complaint

This approach makes your workflow modular. You break a large problem into smaller, manageable sub-problems that can be solved individually. This is possible because you are using structured output first.

Note: This method is often preferable to tool calls in complex systems. Instead of letting an LLM decide whether to use a tool, you use the LLM to classify the request into a category. Then, your code uses simple if/else statements to decide which function (or "tool") to execute. This provides a much clearer and more debuggable log of the LLM's decision-making process.

6. The Recovery Block

Things will inevitably go wrong in a production environment. APIs will be down, LLMs will return nonsense, and you will hit rate limits. You need try/catch blocks, retry logic with exponential backoff, and fallback responses for when things break. This is standard error handling for any reliable application.

The flow looks like this: 1. A request comes in. 2. You check if the operation was a success. 3. If yes, return the result. 4. If no, you can retry the operation (e.g., with a backoff) or trigger a fallback scenario, like notifying the user that the request could not be completed.

Here is a simple illustration in Python:

def get_data_with_fallback(data_dict):
    try:
        # Attempt to access a field that may not exist
        value = data_dict['required_field']
        print("Success! Found the data.")
        return value
    except KeyError:
        # Fallback logic if the key is not found
        print("Field not available. Using fallback information.")
        return "default_fallback_value"

# Example
my_data = {'another_field': 'some_info'}
result = get_data_with_fallback(my_data) 
# Output: Field not available. Using fallback information.

This is a basic example. Proper recovery mechanisms are unique to the problem you are solving and the errors that might arise.

7. The Feedback Block

This block incorporates human oversight and approval into workflows. Some processes are too sensitive or complex to be fully automated by AI agents. Sometimes, you just need a human in the loop to verify an LLM's work before it goes live.

For tasks like sending sensitive customer emails or making purchases, adding approval steps where a human can review, approve, or reject the action is crucial. This is a basic approval workflow where the system comes to a full stop and awaits human input.

The process might look like this: 1. An LLM generates a response or content. 2. Before execution, a human review is triggered (e.g., a Slack notification with "Approve/Reject" buttons). 3. If approved, the action is executed. 4. If rejected, feedback can be provided and sent back to the LLM to repeat the process.

This highlights the importance of humans in the loop, especially for systems that are not simple AI assistants. When a task becomes too tricky, instead of endlessly optimizing your prompt, you may just need to add a human in the loop to ensure safety and quality.

Here’s a conceptual implementation:

def generate_and_approve_content(prompt: str) -> str:
    # 1. Generate content with the LLM
    generated_content = "This is the AI-generated content..."
    print(f"Generated Content:\n{generated_content}")

    # 2. Create a full stop and wait for approval
    # In a real app, this would integrate with a UI, Slack, etc.
    approval = input("Approve this content? (yes/no): ")

    # 3. Continue based on feedback
    if approval.lower() == 'yes':
        print("Final answer is approved.")
        return generated_content
    else:
        print("Workflow not approved. Discarding content.")
        # Optionally, you could capture feedback and retry
        return "Content was rejected by the user."

# Running this function will pause execution until user input is received.

Conclusion

These are the seven building blocks you need to understand to build reliable AI agents. The process is to take a large problem, break it down into smaller sub-problems, and solve each one using these components. Remember to use the intelligence layer—the LLM API call—only when you absolutely cannot solve the problem with deterministic code. By focusing on these fundamentals, you can cut through the hype and start building powerful, production-ready AI systems.