I was building a chatbot that could answer questions about my company’s product documentation. It worked well—until someone asked it to check the current weather to see if our system performance might be affected by a storm. It couldn’t. It was stuck in its text-based world, unable to interact with the outside. This frustrating wall is what led me down the path of building agents. I realized the true power of an LLM isn’t just in what it knows, but in what it can do. Let’s build something that can act.

The leap from a conversational LLM to an autonomous agent is about giving it abilities. Think of the LLM as a brilliant but paralyzed brain. Tools are its hands and senses. A simple tool could be a function that fetches data from an API. The key is making these tools available to the LLM in a way it can understand and decide to use. How do you teach an AI when to use a calculator versus a web search?

We start by defining tools clearly. Each tool needs a name, a description the LLM can read, and the actual code to run. Here’s a basic structure using a modern approach:

from pydantic import BaseModel, Field
from typing import Type, Optional

class ToolSchema(BaseModel):
    """A Pydantic model that defines the input schema for a tool."""
    query: str = Field(description="The search query to look up.")

class WebSearchTool:
    name = "web_search"
    description = "Searches the internet for current information."
    args_schema: Type[BaseModel] = ToolSchema

    def run(self, query: str) -> str:
        # ... Logic to call a search API (e.g., SerpAPI, Tavily)
        api_result = call_search_api(query)
        return f"Search results for '{query}': {api_result}"

The agent’s brain needs to know about these tools. We give it the list and say: “Here are your options. Choose one based on the user’s request.” The LLM then outputs a structured response like {"tool": "web_search", "input": {"query": "weather in London"}}. Our code catches this and runs the corresponding tool. But what happens after the tool runs? The result gets fed back to the LLM so it can formulate a final, informed answer for the user. This loop—thought, action, observation—is the core of an agent.

However, an agent that forgets everything after one turn is useless. You wouldn’t want to remind your assistant of your name in every sentence. This is where memory comes in. We need both short-term memory for the current conversation and long-term memory for important facts. Short-term memory is often a simple list of the recent chat messages. But for long-term memory, how do you store and find relevant past information from thousands of previous interactions?

A common and powerful method is using a vector store. You save past conversations or key facts as text embeddings—numerical representations of meaning. When a new query comes in, you search this “memory bank” for the most semantically similar past entries and feed those to the agent as context. It’s like giving the agent a quick refresher before it answers.

# Simplified example of adding to and querying a vector memory
from langchain_openai import OpenAIEmbeddings
from langchain_chroma import Chroma

embeddings = OpenAIEmbeddings()
vector_store = Chroma(embedding_function=embeddings, persist_directory="./memory_db")

def store_memory(fact: str):
    """Stores a string of information into long-term memory."""
    vector_store.add_texts([fact])

def recall_memory(query: str, k=3) -> list:
    """Finds the k most relevant past memories."""
    return vector_store.similarity_search(query, k=k)

Now, combine these parts. An agent workflow looks like this: Receive a user question. Check memory for relevant past info. Decide if a tool is needed. Use the tool. Process the result. Update memory with new knowledge. Generate an answer. This seems straightforward, but the devil is in the details for a production system. What if the LLM picks a tool that doesn’t exist? What if the API call times out?

Robustness is non-negotiable. You must wrap every tool call in error handling. You need clear logic to handle bad responses from the LLM itself. Setting a maximum number of sequential actions prevents an agent from getting stuck in an infinite loop. You also need to manage “context windows”—the limited number of tokens an LLM can process at once—by strategically summarizing old memories instead of piling everything in. Have you considered what happens when two users ask your agent the same thing at the same time?

Deploying this requires thinking about scale and safety. You’ll want to log every decision, tool call, and outcome for review. Implementing user authentication and rate-limiting prevents abuse. For complex tasks, you might break them down into a plan of smaller steps before executing any of them, which is more reliable than letting the agent figure it out step-by-step. The goal is to move from a cool prototype in a Jupyter notebook to a reliable service that runs 24/7.

The journey from a static language model to a dynamic, tool-using agent is what transforms AI from a source of information into a partner for action. It’s about building a system that can think, remember, and interact. Start small, with one or two tools and a simple memory. See how it changes the interaction. Then, gradually build the safeguards and structure it needs to be trusted.

Was this walkthrough helpful? Did it spark ideas for tools you’d want your own agent to have? I’d love to hear what you’re building—share your thoughts or questions in the comments below. If this guide clarified the path for you, please consider liking or sharing it with others who might be facing the same challenges.