Johannes Hayer

    Building AI Agents with smolagents

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    AI Agents · Online Course

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    The 'Building AI Agents with smolagents' course, designed by Johannes Hayer, is a comprehensive developer-focused guide to mastering the creation of intelligent autonomous systems. As the artificial intelligence landscape transitions from static language models to dynamic, goal-oriented agents, understanding how to construct, deploy, and monitor these systems becomes crucial. This course dives deep into the lightweight and highly efficient smolagents library developed by Hugging Face, offering developers a streamlined path to building production-ready AI agents. Throughout the curriculum, you will explore the foundational theory of AI agents, distinguishing between standard LLM queries, structured workflows, and true agentic behaviors that utilize a "brain and body" design paradigm. Beyond theory, the course provides hands-on experience in building multi-agent systems capable of collaborating to solve complex tasks. You will learn to construct custom tools that empower agents to securely execute code, search the web, and parse complex datasets. Additionally, you'll gain practical experience deploying your agents with intuitive Gradio interfaces directly onto Hugging Face Spaces, while establishing robust observability pipelines using industry-standard tools like OpenTelemetry and LangFuse. Whether you are a software engineer, data scientist, or AI enthusiast, this course equips you with the exact technical skills needed to engineer resilient, self-correcting agentic architectures from scratch.

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    Johannes Hayer

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    Johannes Hayer is a software engineer and AI specialist dedicated to helping developers master the rapidly evolving landscape of agentic workflows. With a deep focus on practical application, Johannes specializes in lightweight, efficient framework architectures, particularly Hugging Face's smolagents. He designs his educational content for engineers who want to cut through the industry hype and build reliable, production-ready AI agents that solve real-world problems. His teaching philosophy centers on a code-first, hands-on approach where abstract concepts like tool calling, agent loops, and system prompts are immediately translated into working code. Johannes has spent years building and optimizing software systems, giving…Show more

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    Program Overview

    Online Course

    Learning format

    AI Agents

    Subcategory

    $19.99 (list $9.99)

    Price

    Price may change · updated within 1–2 weeks

    English

    Course language

    What You'll Learn

    • Differentiate between standard LLM queries, hardcoded workflows, and true autonomous AI agents utilizing the brain-and-body paradigm.
    • Construct functional single-agent and multi-agent systems capable of collaborative problem-solving using the Hugging Face smolagents library.
    • Develop custom tools that allow your AI agents to safely execute sandboxed Python code, search the web, and process unstructured data.
    • Design and deploy interactive user interfaces for your agents using Gradio, and host them live on Hugging Face Spaces.
    • Implement production-grade observability, tracing, and monitoring for agent behaviors using OpenTelemetry and LangFuse.
    • Formulate strategic decision frameworks to determine when to utilize autonomous agents versus traditional deterministic programming workflows.

    Best For

    • Software engineers looking to implement lightweight, efficient agentic workflows.
    • Developers who want to leverage the Hugging Face smolagents ecosystem for production tasks.
    • Data scientists interested in transitioning from static LLM prompting to autonomous system design.
    • Tech practitioners aiming to build observable and secure agent-based applications.

    Not For

    • Absolute beginners who do not have a working knowledge of Python programming.
    • Professionals seeking high-level management theory without hands-on coding requirements.
    • Experts looking for deep theoretical research into advanced reinforcement learning or agent alignment.
    • Those looking for a broad survey of all available AI agent frameworks rather than a deep dive into smolagents.

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