Beyond the Hype: The 7 Foundational Building Blocks for Reliable AI Agents

If you're a developer, then right now it feels almost impossible to keep up with everything that's going on within the AI space. Everyone's talking about AI agents. Your whole LinkedIn and X feeds are full of it. Everyone makes it seem super easy. Yet, you are still trying to figure out, should I use Lang Chain or Llama Index and trying to debug and figure out all of these AI agent systems that you're building and tinkering with.

All of the tutorials that you'll find either are messy or contradicting. And every week there is a new popular article dropping something new where you're like, "Oh do we now also need to know this?" So all in all, it's a complete mess. And the goal with this article is to help calm your AI anxiety and give you clarity on what's currently really going on within the AI space and why you can pretty much ignore 99% of everything that you see online and just focus on the core foundational building blocks that you can use to build reliable and effective agents.

So in this article, I'm going to walk you through the more than 5+ foundational building blocks that you need to understand when you want to build AI agents regardless of what tool you are using. Now I'm going to give these code examples in the Python programming language, but honestly it doesn't matter what tool you use whether that's TypeScript, Java or any other programming language. If you boil it down to these foundational building blocks, you can implement it in anything because they're so simple.

So, I will present these simple code blocks and show you the output and walk you through everything step by step with explanations as well. So that even if you've never written a single line of Python, you can still follow this article. I can guarantee you that after reading this article, you'll have a completely different perspective on what it takes to build effective AI agents and you'll be able to look at almost any problem, break it down, and know the patterns and the building blocks you need in order to solve it and automate it.

The Core Problem: Information Overload

The big problem right now within the AI space, and why you feel so confused as a developer, stems from a simple reality: there is a lot of money flowing into the market. And every time throughout history when there's an opportunity like that, people jump on it to capitalize on it. So even if you're remotely interested in AI, most of your social media feeds will be filled with content that makes it all seem super easy. There are all these tools that you can use to build full agent armies. Yet you are still wondering where to start and how to make this all work in a production-ready environment.

On top of that, you have all of the frameworks and libraries that follow a similar trend—developer tools, GitHub repositories, all kinds of solutions that make it seem super easy to build these AI agents. And then of course we have the news and the plenty of other tools that are built on top of that. This all results in you feeling really overwhelmed and having no idea what's going on and what to focus on.

The Smart Developer's Approach

There's a clear distinction between the top developers and teams that are actually shipping AI systems that make it to production versus the developers that are still trying to debug the latest agent frameworks. Most developers follow all the hype you see on social media, the frameworks, the media attention, and the plethora of AI tools that are out there.

In contrast, smart developers realize that everything you see is simply an abstraction over the current industry leaders: the LLM model providers. Once you realize that as a developer building AI systems and you start to work directly with these model providers' APIs, you realize that you can actually ignore 99% of the stuff that you see online. You'll also realize that fundamentally nothing has pretty much changed since function calling was introduced. Yes, models get better, but the way we work with these LLMs is still the same.

Our code bases from years ago still run. They still work. We only have to change the model endpoints through the APIs because we've engineered them in such a way to not be reliant on frameworks that are essentially built on quicksand. So all this context is super important, just like with LLMs, because otherwise the rest of this article and the core building blocks won't make a lot of sense.

The first most important thing to understand is that if you look at the top teams building AI systems, they use custom building blocks, not frameworks. And that is because the most effective AI agents aren't actually that agentic at all. They're mostly deterministic software with strategic LLM calls placed exactly where they add value.

The problem with most agent frameworks and tutorials out there is that most of them push for giving your LLM just a bunch of tools and letting it figure out how to solve the problem. But in reality, you don't want your LLM making every decision. You want it handling the one thing it's really good at: reasoning with context, while your code or application handles everything else.

The solution is actually quite straightforward: it's just software engineering. So instead of making an LLM API call with more than 12+ solutions, you want to tactfully break down what you're actually building into fundamental components, solve each problem with proper software engineering best practices, and only include an LLM step when it's impossible to solve it with deterministic code.

Making an LLM API call right now is the most expensive and dangerous operation in software engineering. It's super powerful, but you want to avoid it at all costs and only use it when it's absolutely necessary. This is especially true for background automation systems.

This is a super important concept to understand. There is a huge difference between building personal assistants like ChatGPT or Cursor where users are in the loop versus building fully automated systems that process information or handle workflows without human intervention. And let's face it, most of you aren't building the next chat or cursor. You're building backend automations to make your work or your company more efficient.

So when you are building personal assistant-like applications, using tools and multiple LLM calls can be more effective. But when you're building a background automation system, you really want to reduce them. For example, for our production environments, we almost never rely on tool calls. You want to build your applications in such a way where you need as few LLM API calls as possible. Only when you can't solve the problem anymore with deterministic code, that's when you make a call.

And when you get to that point, it's all about context engineering. Because in order to get a good answer back from an LLM, you need the right context at the right time sent to the right model. So you need to pre-process all the available information, prompts, and user inputs so the LLM can easily and reliably solve the problem. This is the most fundamental skill in working with LLMs.

Finally, you need to understand that most AI agents are simply workflows or DAGs (if you want to be precise), or just graphs if you include loops. And most steps in these workflows should be regular code, not LLM calls. What I'm trying to do in this article is really help you understand AI agents from a foundational level, from first principles.

And now that we've set the stage, we get into the foundational building blocks that you need. There are really only seven or so that you use in order to take a problem, break it down into smaller problems, and then try and solve each of those sub-problems with these building blocks that I will introduce to you right now.

1. The Intelligence Layer

Super obvious, right? This is the only truly AI component in there. And this is where the magic happens. This is where you make the actual API call to the large language model. Without this, you just have regular software. The tricky part isn't the LLM call itself; that's super straightforward. It's everything else that you need to do around it.

The pattern here is you have a user input, you send it to the LLM, and the LLM will send a response back to you. We can very easily do this in the Python programming language using, for example, the OpenAI Python SDK.

from openai import OpenAI

client = OpenAI()

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

print(response.choices[0].message.content)

This is the first foundational building block: you need a way to communicate with these models and get information back from them.

2. The Memory Block

This ensures context persistence across your interactions with these models because LLMs don't remember anything from previous messages. They are stateless, and without memory, each interaction starts from scratch. So you need to manually pass in the conversation history each time. This is just storing and passing a conversation state, something we've been doing in web apps forever.

To build on top of the intelligence layer, next to just providing a user input prompt, we also get the previous context and structure that in a conversation-like sequence.

Here's an example of what happens when you don't handle memory correctly. The LLM is stateless and won't know the previous question.

# Incorrect memory handling
from openai import OpenAI

client = OpenAI()

# First call
response1 = client.chat.completions.create(
  model="gpt-4o",
  messages=[{"role": "user", "content": "Tell me a joke about programming."}]
)
print(f"Joke: {response1.choices[0].message.content}")

# Second call without history
response2 = client.chat.completions.create(
  model="gpt-4o",
  messages=[{"role": "user", "content": "What was my previous question?"}]
)
print(f"Follow-up: {response2.choices[0].message.content}")

Output: Joke: Why do programmers prefer dark mode? Because light attracts bugs. Follow-up: I'm unable to recall previous interactions.

Now, here is a proper example of how to handle memory, where we pass in the conversation history. In a real application, you would store and retrieve this from a database.

# Correct memory handling
from openai import OpenAI

client = OpenAI()

conversation_history = [
    {"role": "user", "content": "Tell me a joke about programming."},
    {"role": "assistant", "content": "Why do programmers prefer dark mode? Because light attracts bugs."}
]

conversation_history.append({"role": "user", "content": "What was my previous question?"})

response = client.chat.completions.create(
  model="gpt-4o",
  messages=conversation_history
)

print(response.choices[0].message.content)

Output: Your previous question was asking for a joke about programming. Now it understands the context of the conversation history.

3. Tools for External System Integration

Most of the time, you need your LLM to actually do stuff and not just chat. Pure text generation is limited. You want to call APIs, update databases, or read files. Tools let your LLM say, "I need to call this function with these parameters," and your code handles the actual execution.

The process is: you provide the LLM with tools. For every API call, the LLM decides whether to use a tool. If yes, it selects the tool, and your code is responsible for catching that, executing the tool, and passing the result back to the LLM for it to format the final response.

Tool calling is also directly available in all of the major model providers, so no need for any external frameworks.

import json
from openai import OpenAI

client = OpenAI()

def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    weather_info = {
        "location": location,
        "temperature": "72",
        "unit": unit,
        "forecast": ["sunny", "windy"],
    }
    return json.dumps(weather_info)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_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",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]

messages = [{"role": "user", "content": "What's the weather like in Boston?"}]
response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=tools,
    tool_choice="auto",
)

# ... (code to check for tool call, execute it, and send result back) ...
# For brevity, the full tool-calling loop is omitted, but the principle is shown.
# The LLM would respond with a tool call to `get_current_weather` with `location='Boston'`.

With this, we augment the LLM beyond just text generation capabilities. Through tools, we give the model a way to integrate and connect with external systems.

4. Validation for Quality Assurance

If you want to build effective applications around large language models, you need a way to make sure 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 the validation fails, you can send it back to the LLM to fix it. This concept is known as structured output.

We need that structured output, which is super crucial so we can engineer systems around it. Rather than just asking a question and getting text back, we want a predefined JSON schema where we are 100% sure that what we're getting back contains the actual fields we can use in our application.

The process looks like this: We ask an LLM to provide structured output (JSON). We validate it against a schema using a library like Pydantic. If it's valid, we have our structured data. If not, we take the error and send it back to the LLM to correct it.

from openai import OpenAI
from pydantic import BaseModel

client = OpenAI()

class TaskResult(BaseModel):
    task: str
    priority: str

response = client.chat.completions.create(
    model="gpt-4o",
    response_model=TaskResult,
    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."}
    ]
)

print(response)

Output: task='complete the project presentation by Friday' priority='high' We now have a validated data object where we can programmatically call response.task or response.priority. With techniques like this, we can validate both the incoming data we send to the LLM and the outgoing data the LLM sends back.

5. Control for Deterministic Flow

You don't want your LLM making every decision. Some things should be handled by regular code. You can use if-else statements, switch cases, and routing logic to direct the flow based on conditions. This is just normal business logic.

For example, we can use an LLM to classify an incoming message's intent (e.g., question, request, complaint). Then, our application can use simple if-statements to route the message to the correct handler function. We make our workflow modular, breaking a big problem into smaller sub-problems that we can solve individually.

from openai import OpenAI
from pydantic import BaseModel, Field
from typing import Literal

client = OpenAI()

class Intent(BaseModel):
    intent: Literal['question', 'request', 'complaint']
    confidence: float = Field(..., ge=0, le=1)
    reasoning: str

def handle_question(msg): print(f"HANDLING QUESTION: {msg}")
def handle_request(msg): print(f"HANDLING REQUEST: {msg}")
def handle_complaint(msg): print(f"HANDLING COMPLAINT: {msg}")

def classify_and_route(user_message):
    response = client.chat.completions.create(
        model="gpt-4o",
        response_model=Intent,
        messages=[
            {"role": "system", "content": "Classify the user's intent."},
            {"role": "user", "content": user_message}
        ]
    )

    print(f"Input: '{user_message}' -> Intent: {response.intent}, Reasoning: {response.reasoning}")

    if response.intent == 'question':
        handle_question(user_message)
    elif response.intent == 'request':
        handle_request(user_message)
    elif response.intent == 'complaint':
        handle_complaint(user_message)

classify_and_route("What is machine learning?")
classify_and_route("Please schedule a meeting for tomorrow.")
classify_and_route("I'm unhappy with the service quality.")

Output: Input: 'What is machine learning?' -> Intent: question, Reasoning: The input asks for information or explanation about a concept. HANDLING QUESTION: What is machine learning? Input: 'Please schedule a meeting for tomorrow.' -> Intent: request, Reasoning: The user is asking for an action to be performed. HANDLING REQUEST: Please schedule a meeting for tomorrow. Input: 'I'm unhappy with the service quality.' -> Intent: complaint, Reasoning: The user is expressing dissatisfaction with a service. HANDLING COMPLAINT: I'm unhappy with the service quality. This is all possible because we are using structured output. We get a data model back, and our code can use simple if-else statements to check the intent field and route accordingly. This is often more robust and debuggable than relying on LLM tool-calling for complex workflows.

6. Recovery for Reliability

Things will go wrong in production. APIs will be down. LLMs will return nonsense. Rate limits will hit you. You need try-catch blocks, retry logic with back-off, and fallback responses when stuff breaks. This is building reliable applications 101—standard error handling.

The flow could be: a request comes in. We check if it's a success. If yes, return the result. If no, we can retry with a back-off or trigger a fallback scenario, like letting the user know you can't help right now.

def get_data_with_fallback():
    data = {"info": "some data"} # Let's pretend this came from a failed API call
    try:
        # This will fail because 'details' key doesn't exist
        result = data["details"]
        print("Success! Got details.")
        return result
    except KeyError:
        print("Key not found. Using fallback information.")
        return "General output or standard reply."

print(get_data_with_fallback())

Output: Key not found. Using fallback information. General output or standard reply. This is a very simple illustration. This can become infinitely complex, as every try-except block will be unique to the problem you're trying to solve.

7. Feedback for Human Oversight

Some processes are just too tricky right now to be fully handled by AI agents. Sometimes you just want a human in the loop to check an LLM's work before it goes live.

When a task is too important or complex for full automation (like sending sensitive emails or making purchases), adding approval steps where humans can review and approve or reject before execution is crucial. This is a basic approval workflow.

The process involves the LLM generating a response, then pausing for human review. A human gets a notification (e.g., in Slack) with "approve" or "reject" buttons. If approved, the process continues. If rejected, feedback can be sent back to the LLM to repeat the process.

Here is a simple terminal-based example to illustrate the concept of a full stop for approval.

def generate_content_with_approval():
    generated_content = "Here is some important, AI-generated content."
    print(f"Generated Content: {generated_content}")

    approval = input("Approve this content? (yes/no): ")

    if approval.lower() == 'yes':
        print("Final answer is approved. Continuing workflow...")
    else:
        print("Workflow not approved. Stopping.")

generate_content_with_approval()

In a real application, you would integrate this with a front-end or a messaging platform like Slack using webhooks. The principle is the same: create a full stop before sending something off.

Conclusion

Those are the foundational building blocks you need to understand in order to build reliable AI agents. You take a big problem, break it down into smaller problems, and for every smaller problem, you try to solve it using these building blocks, only using an LLM API call—the intelligence layer—when you absolutely cannot get around it. By focusing on these fundamentals, you can cut through the noise and build robust, production-ready AI systems.