LEARN FROM THE BEST
TRAINERS WORLDWIDE

AI agents can do much more today than generate code. Used well, they can read code, inspect HTTP responses, analyze build logs, evaluate deployment metadata, and help systematically find performance, cost, and infrastructure issues.

In this 3-hour workshop, we build a practical agent workflow for web applications. We start with an external audit of a running app, then analyse common issues in a Next.js codebase and connect the findings with infrastructure data such as logs, deployments, caching behaviour, and metrics. The Vercel CLI is used as a concrete example, but the concepts also apply to other platforms with a CLI or API, such as Cloudflare, Netlify, or AWS.

Participants will learn how to create repeatable agent skills for audits and debugging, how to build safe read-only workflows, and how to structure results so they lead to concrete technical actions instead of vague recommendations.

Stop prompting. Start orchestrating. In four intense hours you'll go from using Claude Code like a faster autocomplete to commanding it like a senior engineer commands a team: engineering its context, deploying fleets of subagents, locking it down with hooks, and turning it loose on work that runs without you.

Every Claude Code user hits a ceiling where the easy wins run out. The agent handles small stuff beautifully, then loses the thread on anything real. The difference between that ceiling and real mastery isn't better prompts. It's control. This workshop is about control.

You'll spend the whole four hours inside CLASH, a real full-stack application, handed to you fully built so nothing stands between you and the hard parts. A serious codebase is the point: it's the only place agentic engineering shows you whether it actually holds up.

The throughline is context. Treated carelessly, the context window fills with noise until the agent drifts. Treated as a resource you engineer, it becomes the biggest lever you have. From there the toolkit opens up. Repeatable work becomes a reusable Skill. Noisy, exploratory work goes to subagents that run in their own isolated context, several at once when the job allows. Hard rules become hooks the agent cannot cross. Your own systems come into reach through MCP.

Then you let go of the wheel, carefully. The same agent that pairs with you can run headless in a pipeline, drive a long task to a defined finish on its own, or live inside your software through the Agent SDK. We close by setting two greenfield methodologies, Spec Kit and BMAD, side by side, so you leave knowing not just how to drive the agent but which approach fits which problem.

Two ideas hold it together: context is king, and you push it, you own it. This was never about generating code faster. It's about staying in command while the agent does more.

This is an advanced session for engineers, tech leads, and architects who already use Claude Code every day and want to reach the top of the curve. We move fast, and we start in the deep end.

In this hands-on workshop, you’ll learn how to integrate AI directly into your application using TanStack AI. We’ll build a working AI chat feature end-to-end, starting from server setup and finishing with a streaming client, tool calling, human-in-the-loop workflows, and real debugging using TanStack DevTools.

Through guided exercises, you’ll learn how to set up TanStack AI on the server, connect a client application to a streaming AI backend, build a functional chat interface, create your first AI tools, and implement approval flows so humans can stay in control when tools are invoked.

By the end of the session, you’ll understand the core building blocks of AI-powered applications and walk away with a solid foundation for adding intelligent chat and tool capabilities to your own apps.

Workshop outcomes

When you're finished with this workshop you will:

• Understand how to set up TanStack AI on the server and wire it to a provider (like OpenAI)
• Know how to connect a client app to a streaming AI endpoint and handle incremental updates cleanly
• Build a functional chat UI with a solid state model for messages, streaming tokens, and tool results
• Create and use your first AI tools with clear inputs, outputs, and predictable behavior
• Build human-in-the-loop workflows with tool approvals so your app can ask for confirmation before executing sensitive actions
• Debug and inspect your AI app using TanStack DevTools, including tool calls, responses, and streaming behavior

What you'll learn

TanStack AI gives you the primitives to build real AI features, not just a demo prompt box. This workshop focuses on wiring everything together properly, from server-side streaming to client UX, then layering in tools, approvals, and debugging so the final result is something you can confidently evolve into production features.

You’ll learn the following through these exercises:

• Server setup - Configure TanStack AI on the server, connect to your model provider, and expose endpoints that support streaming chat
• Streaming and SSE - Implement and consume streaming responses, understand the lifecycle of a stream, and build UI that stays responsive while tokens arrive
• Chat UI and state - Build a chat interface that handles message history, partial responses, loading states, and tool outputs in a clean way
• Tools - Define tools, validate inputs, return structured outputs, and integrate tool results back into the conversation flow
• Human in the loop - Add approval steps for tool execution, implement “approve/deny” flows, and keep users in control when actions matter
• Debugging with TanStack DevTools - Inspect requests, responses, tool calls, timing, and streaming behavior so you can troubleshoot fast and iterate safely

Prerequisites

This workshop assumes you can build and run a React + TypeScript app locally and you are comfortable working with a basic server setup.

• Basic understanding of SSE and streaming is required (we’ll use streaming heavily throughout the workshop)
• Experience with React is required (components, state, props, rendering lists)
• Basic TypeScript knowledge is required (we’ll rely on types for tools and structured outputs)
• You will need an OpenAI API key with available credits to use during the workshop exercises

This workshop bridges the long-standing gap between React developers and production-ready delivery. Many front-end developers know how to build great interfaces but they stumble when it comes to building, testing, deploying, and monitoring those apps in real-world environments.

This workshop gives React developers the DevOps superpowers they need.

You’ll learn how to:

• Dockerize your React application for development and production following best practices from Docker Captain Leader.
• Build scalable CI/CD pipelines using GitHub Actions
• Optimize your apps for performance and reliability

This is not theory. It’s not a shallow overview. This is a production-focused, real-world workshop by a front-end developer who lives and breathes this every day.