Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.

Learn how to collaborate with AI agents to streamline coding workflows, generate and modify code across multiple files, and enhance development efficiency. Learn how to collaborate with AI agents to streamline coding workflows, generate and modify code across multiple files, and enhance development efficiency.Understand how AI coding agents combine tool usage, context awareness, and human-action tracking to function effectively and get a deep dive into search and discovery as one of the main challenges of AI coding agents. Understand how AI coding agents combine tool usage, context awareness, and human-action tracking to function effectively and get a deep dive into search and discovery as one of the main challenges of AI coding agents.Use Windsurf to do real-world coding tasks, including debugging JavaScript code, updating an old large codebase, and building a Wikipedia analysis app that retrieves, processes, and visualizes data while learning to manage unexpected AI behavior. Use Windsurf to do real-world coding tasks, including debugging JavaScript code, updating an old large codebase, and building a Wikipedia analysis app that retrieves, processes, and visualizes data while learning to manage unexpected AI behavior._Join our latest course Build Apps with Windsurf’s AI Coding Agents, built in partnership with Windsurf and taught by Anshul Ramachandran.AI-assisted Integrated Development Environments, or IDEs, make coding and development workflows faster, more efficient, and more fun.Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Using an AI coding agent effectively requires a new approach to prompting, iteration, and structuring development tasks.In this course, you’ll learn the inner workings of coding agents and how to use Windsurf to code and build several applications. In details, you’ll:Learn how to get started with Windsurf, and build your first app (a snake game) in a couple of minutes.Look at the evolution of AI coding assistants, from simple code suggestions to agentic systems that collaborate with developers.Use an AI agent to analyze and resolve issues in Javascript code, like debugging test issues and multi-file refactoring.Learn mental models to understand how coding agents combine human-action tracking, tool integration, and context awareness in their agentic coding workflow.Learn the challenges of code search and discovery of AI coding agents and how a multi-step retrieval approach helps overcome them.Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. Use Windsurf to build a Wikipedia data analysis app — retrieving, parsing, and analyzing word frequencies.Enhance the performance of your Wikipedia app by adding caching and while doing so learn how to course-correct when the AI agent produces unexpected results.Learn tips and tricks such as command, autocomplete, @mentions, and explain functionalities to maximize your interactions with the overall AI system as you build a full-stack app to visualize the data of your Wikipedia analysis app.Leverage the multimodal capabilities of the AI agent to increase your development velocity with an example in which you upload a mockup with sketched-out reference UI features to add new functionalities to your app.By the end of this course, you’ll understand agentic coding in-depth and learn to use it to make your development process faster, more efficient, and more enjoyable._It’s helpful for anyone familiar with coding to have a basic understanding of LLM prompting and AI-assisted application development.

Build Apps with Windsurf’s AI Coding Agents

Learn a flexible framework to build a variety of complex AI applications.

Learn a flexible framework to Learn how Haystack components, pipelines, and document stores can be used to build custom AI applications.build custom AI applications with components, pipelines, and document stores Learn how Haystack components, pipelines, and document stores can be used to build custom AI applications.Build a RAG pipeline and extend its ability by creating custom components. Build a RAG pipeline and extend its ability by creating custom components.Use conditional branching, self-reflecting agents, and function calling to build complex pipelines and apps. Use conditional branching, self-reflecting agents, and function calling to build complex pipelines and apps._In Building AI Applications with Haystack you will learn a high-level orchestration framework that helps ensure your applications are flexible, extendible, and maintainable, even as the technology stack changes, user needs arise, and new features are added.Using a framework can provide common features out of the box that significantly speeds up the development process. Haystack offers robust and flexible architecture and framework for building AI applications. It manages complexity and helps you focus more on developing your application at a higher level of abstraction.Throughout the course, you will develop several projects, including a RAG app, a news summarization app, a chat agent with function calling, and a self-reflecting agent with loops.What you’ll do: Learn about the core abstractions and unique building blocks of the Haystack framework and see how these elements can be combined for various AI use cases.Build a RAG pipeline by using Haystack components, pipelines, and document stores.Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs.Use conditional routing to create a branching pipeline with a fall back to web-search when the LLM does not have the context needed to fully respond to the user’s query.Build a self-reflecting agent for named entity recognition with a Haystack pipeline that is able to loop using an output validator custom component.Create a chat agent using OpenAI’s function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent’s capabilities.Start building exciting LLM applications and optimizing your development workflow using Haystack._Anyone who has basic Python knowledge and wants to learn to build and customize complex apps using Haystack!

Building AI Applications With Haystack

Learn how an AI Assistant is built to use and accomplish tasks on computers.

Learn about Anthropic’s family of models, its approach to AI research, and the best way to prompt it, including multi-modal use cases. Learn about Anthropic’s family of models, its approach to AI research, and the best way to prompt it, including multi-modal use cases.Learn effective prompting techniques, implement prompt caching to reduce costs and latency, and build AI applications that can call tools. Learn effective prompting techniques, implement prompt caching to reduce costs and latency, and build AI applications that can call tools.See how you can combine multimodal capabilities, agentic workflows, and tool use to build an AI assistant that can navigate and interact with computer interfaces, executing tasks like web searches. See how you can combine multimodal capabilities, agentic workflows, and tool use to build an AI assistant that can navigate and interact with computer interfaces, executing tasks like web searches._Building Towards Computer Use with Anthropic introduces an innovative capability from Anthropic that enables models to interact with and navigate computer interfaces.Taught by Colt Steele, Anthropic’s Head of Curriculum, this course covers Anthropic’s family of models and the building blocks that lead to the amazing new application – Computer Use. Computer Use utilizes the capabilities of the latest models including image reasoning and tool use to enable an LLM-based agent to use a computer. Like a human user, the model processes an image of the screen, analyzes it to understand what’s going on, and navigates the computer by issuing mouse clicks and generating keyboard strokes to get things done.In this course, you’ll learn the features that lead up to computer use from working with the Anthropic’s API, to multimodal prompting, prompt caching, and tool use, ending in a demo that combines these features to build an AI assistant that uses a computer.In detail, you’ll:Learn Anthropic’s approach to AI research, principles of AI safety, alignment, and interpretability while understanding the key differences between its models.Make API requests to Claude, format messages for better responses, and control API parameters like system prompts, temperature, and max tokens for optimal responses.Write multi-modal prompts that combine text and image content blocks and build with streaming responses.Learn effective prompting techniques such as using prompt templates, structuring prompts in XML, and providing examples to get consistent high-quality responses.Learn to implement prompt caching and see how it can reduce costs and latency.Understand tool-use workflows and build a chatbot that can call different tools in response to users’ queries.See all these concepts come together in a demo that uses Anthropic Computer Use to achieve a task on a computer.Start utilizing Anthropic’s family of models to build towards Computer Use applications._Anyone who has basic Python knowledge, wants to learn how to use all of the features of the Anthropic family of models, and understand the capabilities of computer use applications. 

Building toward Computer Use with Anthropic

Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments.

Learn how to add observability to your agent to gain insights into its steps and know how to debug it. Learn how to add observability to your agent to gain insights into its steps and know how to debug it.Learn how to set up evaluations for the agent components by preparing testing examples, choosing the appropriate evaluator (code-based or LLM-as-a-Judge), and identifying the right metrics. Learn how to set up evaluations for the agent components by preparing testing examples, choosing the appropriate evaluator (code-based or LLM-as-a-Judge), and identifying the right metrics.Learn how to structure your evaluation into experiments to iterate on and improve the output quality and the path taken by your agent. Learn how to structure your evaluation into experiments to iterate on and improve the output quality and the path taken by your agent._Learn how to systematically assess and improve your AI agent’s performance in Evaluating AI Agents, a short course built in partnership with Arize AI and taught by John Gilhuly, Head of Developer Relations, and Aman Khan, Director of Product.When you’re building an AI Agent, an important part of the development process is evaluations or evals. Whether you’re building a shopping assistant, coding agent, or research assistant, having a structured evaluation process helps you refine its performance systematically—rather than relying on trial and error. With a systematic approach, you structure your evaluations to assess the performance of each component of the agent, as well as its end-to-end performance. For each component, you select the appropriate evaluators, testing examples, and metrics. This process helps you identify any areas of improvement so you can iterate on your agent during development and in production. In this course, you’ll build an AI agent, add observability to visualize and debug its steps, and evaluate its performance component-wise.In detail, you’ll: Distinguish between evaluating LLM-based systems and traditional software testing.Explore the basic structure of AI agents – routers, skills, and memory – and implement an AI  agent from scratch.Add observability to the agent by collecting traces of the steps taken by the agent and visualizing the traces.Choose the appropriate evaluator – code-based, LLM-as-a-Judge, and human annotations – for each component of the agent. Set up evaluations for the skills and router decisions of the agent example using code-based and LLM-as-a-judge evaluators, by creating testing examples from collected traces and preparing detailed prompts for the LLM-as-a-judge.Compute a convergence score to evaluate if the example agent can respond to a query in an efficient number of steps.Run structured experiments to improve the performance of the agent by exploring changes to the prompt, LLM model, or the agent’s logic.Understand how to deploy these evaluation techniques to monitor the agent’s performance in production.By the end of this course, you’ll know how to trace AI agents, systematically evaluate them, and improve their performance. _Anyone who has basic Python knowledge and wants to learn to evaluate, troubleshoot, and improve AI agents effectively—both during development and in production. Familiarity with prompting an LLM model would be helpful but not required.