Building Reliable AI Agents: The 7 Foundational Blocks Explained in 10 Minutes

If you're a developer, it feels almost impossible to keep up with everything going on in the AI space. Everyone's talking about AI agents. Your social media feeds are full of it, and everyone makes it seem super easy. Yet, you are still trying to figure out whether to use Lang Chain or Llama Index while debugging the AI agent systems you're tinkering with.

The tutorials you find are often messy or contradictory. Every week, there's a new groundbreaking release that makes you wonder, "Do we now also need to know this?" All in all, it's a complete mess.

The goal of this article is to help calm your AI anxiety and give you clarity on what's currently happening in the AI space. You can ignore 99% of what you see online and just focus on the core foundational building blocks to create reliable and effective agents.

This article will walk you through the seven foundational building blocks you need to understand when you want to build AI agents, regardless of your chosen tools. The code examples are in Python, but the principles are universal. Whether you use TypeScript, Java, or any other programming language, these simple building blocks can be implemented anywhere.

After reading this article, you'll have a completely different perspective on what it takes to build effective AI agents. You'll be able to look at almost any problem, break it down, and know the patterns and building blocks needed to solve and automate it.

The Core Problem: Hype vs. Reality

There is a lot of money flowing into the AI market. Historically, whenever such an opportunity arises, people jump on it to capitalize. Your social media feeds are likely filled with tools that promise to build full agent armies, making it all seem incredibly easy. Yet, you're left wondering where to start and how to make it all work in a production-ready environment.

On top of that, numerous frameworks, libraries, and developer tools follow a similar trend, making it seem simple to build these AI agents. This constant influx of news and tools results in feeling overwhelmed, with no clear idea of what to focus on.

There's a clear distinction between the top developers and teams shipping AI systems to production versus those still debugging the latest agent frameworks. Most developers follow the hype—social media trends, new frameworks, and the plethora of AI tools. In contrast, smart developers realize that everything you see is simply an abstraction over the current industry leaders: the LLM model providers.

Once you, as a developer, start working directly with these model providers' APIs, you realize you can ignore 99% of the noise online. Fundamentally, not much has changed since function calling was introduced. Yes, models get better, but the way we work with these LLMs remains the same. Codebases from two years ago still run; the only change needed is updating the model endpoints through the APIs because they were engineered to not be reliant on frameworks built on quicksand.

The Foundation: Custom Building Blocks, Not Frameworks

The most effective AI agents aren't as "agentic" as they seem. They are mostly deterministic software with strategic LLM calls placed exactly where they add value. The problem with most agent frameworks and tutorials is that they push for giving an LLM a bunch of tools and letting it figure out how to solve a problem. 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 handles everything else.

The solution is straightforward software engineering. Instead of making an LLM API call with 15 tools, tactfully break down what you're 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 with deterministic code.

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

There's 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 without human intervention. Most developers are building backend automations to make their work or company more efficient. For personal assistant applications, using tools and multiple LLM calls can be effective. For background automation, you want to reduce them.

Build your applications to require as few LLM API calls as possible. Only when you can't solve the problem with deterministic code should you make a call. At that point, it's all about context engineering. To get a good answer from an LLM, you need the right context, at the right time, sent to the right model. You must pre-process all 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.

Most AI agents are simply workflows (or DAGs, to be precise). Most steps in these workflows should be regular code, not LLM calls. Now, let's get into the foundational building blocks.

The 7 Foundational Building Blocks for AI Agents

There are really only about seven building blocks you need to take a problem, break it down, and solve each sub-problem.

1. The Intelligence Layer

This is the only truly "AI" component. It's where the magic happens—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, but everything you need to do around it. The pattern is simple: a user input is sent to the LLM, and the LLM sends a response back.

Here's a simple Python example using the OpenAI 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: a way to communicate with models and get information back.

2. Memory

This building block ensures context persistence across interactions. LLMs are stateless; they don't remember previous messages. Without memory, each interaction starts from scratch. You need to manually pass in the conversation history each time. This is just storing and passing a conversation state, something common in web development.

The process involves providing the user input along with the previous context, structured as a sequence of messages. You then get the response and handle updating the conversation history.

Here's an example in memory.py showing incorrect and correct ways to handle memory:

# Incorrect: No memory handled
# AI is asked a follow-up question without context
# It won't know the previous question.

# Correct: Handling memory
def ask_with_memory(history):
    # history is a list of user/assistant messages
    # New user question is appended
    # The whole history is sent to the LLM
    pass # Implementation details omitted for brevity

When demonstrated, asking a joke works, but a follow-up like "What was my previous question?" fails without memory.

Output (without memory): ```

Why do programmers prefer dark mode? Because light attracts bugs. What was my previous question? I'm unable to recall previous interactions. ```

With proper memory handling, where the history is passed down, the LLM can answer correctly.

Output (with memory): ```

Your previous question was asking for a joke about programming. ```

3. Tools

Most of the time, you need your LLM to do stuff, 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 flow is: the LLM receives a prompt, memory, and a list of available tools. It decides whether to use a tool. If yes, it selects the tool, and your code executes it. The result is then passed back to the LLM to format a final text answer.

Tool calling is directly available in major model providers. You specify a function, transform it into a tool schema, and make it available to the LLM.

# Example of a tool definition for getting weather
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"],
            },
        },
    }
]

Your code checks if the LLM decided to call the tool, passes the parameters, runs the function, and sends the result back to the LLM. With this, an LLM can use the get_weather function to provide weather information for any city, augmenting its capabilities beyond its training data.

4. Validation

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 (e.g., using Pydantic). If validation fails, you can send it back to the LLM to fix it. This concept is known as structured output.

Instead of getting plain text back, you want a predefined JSON schema, ensuring you receive fields you can use in your application. The process is: ask the LLM for structured JSON output, validate it against a schema, and if it's invalid, send the error back to the LLM for correction.

Let's say you're building a task management tool. You can define a specific data structure using Pydantic:

from pydantic import BaseModel

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

Getting structured output is supported by major model providers. In the OpenAI API, you can specify a response format.

# Simplified API call
response = client.chat.completions.create(
  model="gpt-4o",
  messages=[...],
  response_format={"type": "json_object", "schema": TaskResult.model_json_schema()}
)

With a prompt like "I need to complete the project presentation by Friday, it's high priority," you get a validated data object back that you can programmatically access.

5. Control

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

For example, you can use an LLM to classify an incoming message's intent (e.g., question, request, complaint) using structured output. Then, your application uses simple if statements to route the request to the correct function. This makes your workflow modular, breaking a big problem into smaller, solvable sub-problems.

Here's a code example using Pydantic to define the intent:

from pydantic import BaseModel, Field
from typing import Literal

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

Based on the classified intent, you can call a specific function. This is more robust than relying on tool calls, especially for complex systems. When you use a classification step with reasoning, you get a full log of the LLM's decision-making process, which is invaluable for debugging.

Example Output: ``` Input: What is machine learning? Intent: question

Reasoning: The input asks for information or an explanation about a concept.

Input: Please schedule a meeting for tomorrow. Intent: request

Reasoning: The user is asking for an action to be performed.

Input: I'm unhappy with my service quality. Intent: complaint Reasoning: The user is expressing dissatisfaction. ```

6. Recovery

Things will go wrong in production. APIs will be down, LLMs will return nonsense, and you'll hit rate limits. You need try-catch blocks, retry logic with backoff, and fallback responses. This is standard error handling for any production system.

The flow is: a request comes in. You check for success. If it succeeds, you return the result. If it fails, you can retry with a backoff or trigger a fallback scenario, like informing the user you can't help at the moment.

In Python, you can use try-except blocks:

try:
    # Code that might fail
    result = data['required_key']
except KeyError:
    # Fallback logic
    result = "Using fallback information."
    print("Key not available. " + result)

Every try-except block will be unique to the problem you're solving and the errors that might arise.

7. Feedback

Some processes are too critical to be fully automated. You need human oversight. When a task is too important or complex (like sending sensitive emails or making purchases), add approval steps where a human can review, approve, or reject the LLM's work before execution.

This is a basic approval workflow. For example, an LLM generates a response. Before it's sent, a human gets a notification (e.g., in Slack) with "Yes" or "No" buttons. If approved, the action is executed. If rejected, feedback can be provided to the LLM to adjust and repeat the process.

This highlights the importance of humans in the loop, distinguishing AI assistants from fully autonomous systems. Often, instead of endlessly optimizing a prompt, adding a human-in-the-loop is the safer, more practical solution.

You can implement this with a strategic pause in your code, waiting for approval.

def get_human_approval(content):
    print("--- Generated Content ---")
    print(content)
    print("-------------------------")
    approval = input("Approve this content? (yes/no): ")
    return approval.lower() == 'yes'

# In your workflow
generated_content = "This is some AI generated text."
if get_human_approval(generated_content):
    print("Final answer is approved.")
else:
    print("Workflow not approved.")

This creates a full stop, waiting for a human decision before proceeding.

Conclusion

These are the seven building blocks you need to build reliable AI agents. The process is to take a large problem, break it down into smaller ones, and for each smaller problem, try to solve it using these building blocks, only using the intelligence layer (an LLM API call) when you absolutely cannot get around it. By focusing on these fundamentals, you can cut through the hype and build robust, production-ready AI systems.