In this workshop, you'll learn how to build a long-running AI agent harness by combining Cursor and the Claude Code SDK.We'll start by using Cursor to plan out a full application through conversation, defining features, architecture, and requirements. Then, we'll take that plan and adapt Anthropic's Claude Code SDK agent architecture to execute it. You'll see how to modify the SDK's prompts and workflow to align with your specific project.By the end of the session, you'll watch a live agent handle context management, write progress logs, and maintain state across sessions, all the patterns Anthropic outlined for building agents that don't lose track of their work.You'll walk away with a working workflow you can apply to your own projects.

Join a focused 2-hour session covering MCP's purpose, architecture, hands-on server implementation, and future directions. Designed for developers and system architects aiming to integrate contextual data with ML models effectively, using MCP's latest 2026 features like semantic routing and federated context.Agenda:- Introduction & Why MCP? Key challenges MCP solves and core benefits, including 20x throughput from federated layers.- Architecture Deep Dive: Components, interactions, scalability principles, plus quantum-resilient encryption in MCP 2.1.- Building Your Own MCP Server: Guided walkthrough with code snippets and best practices; live demo of multimodal data fusion.- Future of MCP Developments: Potential enhancements like agentic workflows, emerging trends, and real-world scenarios.Key Takeaways:- Clear understanding of MCP's rationale.- Insight into design patterns and scaling considerations.- Practical steps to implement a prototype server.- Awareness of upcoming trends and how to apply MCP in projects.

Cole will walk through his agentic coding workflow that allows him to delegate all coding to the agent while still keeping him in the driver's seat (no vibe coding!). The key is to create a structured approach for both the up front planning and the validation - and he'll demonstrate what that looks like. Plus, he'll show the true power of creating a system for AI coding - with a proper system, every coding mistake the LLM makes is an opportunity to not just address the bug manually, but fix the system so the bug doesn't happen again. That's what makes your coding agent more and more reliable over time!