Claude Code: An agentic software experience

Experience software engineering with AI agents as genuine teammates.

Description

Agentic engineering seems to finally deliver on the promise of notable productivity gains from AI. But what does it actually feel like to work in a team where AI agents are members? How do you steer and direct them? What patterns and practices emerge when you have multiple agents working in parallel on the same codebase?

In this hands-on and immersive workshop, you'll receive a backlog. For the rest of the day, AI agents work alongside you as full team members. Building, testing, refactoring, and documenting in parallel while you steer. You are not invoking AI as a tool. Instead AI is your teammate: agents that take initiative, pick up tasks, and deliver results. Your role is to direct, review, and iterate.

Every hour, a short knowledge pulse introduces a concept grounded in what you have just experienced: hooks, MCP plugins, agentic patterns. Theory follows practice, not the other way around. Coaches are available throughout the day. You leave with working software and a clear sense of what agentic development feels like in practice.

Intended for anyone who builds software or wants to: developers, testers, product owners, analysts, and enthusiasts. Solo participants and teams alike. Organizations can enroll their own team and work in a context that is relevant to them.

Come with an active, Claude license. Expect to use between 5 and 20 million tokens during the day. Don't have one? Contact us and we will arrange a day license for you.

Learning Goals

• Understand the principles behind agentic development [Understand]
• Apply directing AI agents during a software development session [Apply]
• Explain common agentic patterns [Understand]
• Practice engineering in a team where AI agents are members [Apply]
• Determine when agentic development adds value [Evaluate]

Subjects

• What is agentic development, and how does it differ from using AI as a tool?
• Working through a backlog with AI agents as team members
• Directing and steering agents effectively
• Agentic patterns: hooks, MCP plugins, multi-agent coordination
• Parallel workflows: building, testing, refactoring, documenting with agents
• Reflection: when does agentic development add value, and when doesn't it?