A software system that orchestrates multiple processing steps is referred to as an agent. An agent can traverse a wide solution space, handle decision-making logic, remember intermediate steps, and determine which actions to take and in what order. This enables agents to complete difficult, broad-ranging tasks that are out of the realm of possibility for traditional software. Agents shine when your application requires iterative workflows, adaptive logic, or exploring multiple pathways. For simpler use cases like basic queries, an LLM alone may be enough. However, agents offer memory and planning capabilities, tool access, and longevity and learning through iterative feedback. Ultimately, the decision to build an agent depends on the task's complexity, available resources, and the added value an agent can bring. Function calling is a key ingredient in many agent systems, where an LLM outputs structured data that maps to specific actions or APIs. This process bridges natural language with programmatic actions. Single-step choice allows the model to select the appropriate function based on user input, while structured output follows a specific format making it easier to parse and reducing errors. Scalability is also achieved by adding more functions to handle additional tasks without altering core logic. For more complex tasks, you can expand the LLM's role to include iterative reasoning and tool usage. This includes single call with function calling for straightforward tasks where the LLM needs to pick the right tool once, and agent with memory and multi-step reasoning for orchestrating complex workflows and calling different tools as new information comes in. Both approaches leverage function calling but differ in their layer of logic, memory, and iterative planning. You can either build your agent from scratch or use a framework like smolagents, LangGraph, or AutoGen, which handle common challenges such as state, routing, and skill descriptions while offering established best practices and examples. Frameworks are helpful for quick setup, having great resources, and integrating seamlessly into orchestration libraries. However, they may reduce flexibility, be opinionated in their designs, and cause lock-in concerns when switching away. For highly specialized or large-scale architectures, coding your own agent might be a better fit as it allows fine-tuning every layer and avoiding limitations imposed by a framework's design. The rest of the guide will show how to set up an example agent using smolagents, AutoGen, or LangGraph. Smolagents is great because it has pre-built common agents, seamless integration with Hugging Face tools and infrastructure, and a flexible architecture that allows for both simple function-calling agents and more intricate workflows. By following the steps outlined in this guide, you can build your own functioning AI agent using smolagents, AutoGen, or LangGraph. You'll learn how to set up an example agent, explore how quickly you can get an agent running using Hugging Face's smolagents, walk through the setup, configuration, and a simple example to highlight just how straightforward it can be. The guide will cover building an agent using smolagents, AutoGen, or LangGraph, including installing required libraries, importing essential building blocks, setting up OpenAI API key and model, creating helper functions for extracting content, defining nodes, and compiling the workflow, as well as invoking the workflow and retrieving final output. By following this guide, you'll have a basic agent that can tackle real-world tasks with minimal manual intervention. As the space evolves, keep experimenting, stay flexible, and refine your approach to deliver the best possible user experience.