There is a gap between experimenting with AI on a side project and systematically delivering AI-assisted code in production. Track 3 closes that gap with a methodology that holds under deadline pressure.
Most developers who experiment with AI get impressive first drafts and then a production problem. Tests pass but the code behaves differently in production. Context truncates silently in the middle of a critical function. Accountability is diffuse when something goes wrong. These are structural problems, not tool problems.
An eight-part series — required pre-reading for Track 3. The agentic loop, VS Code setup, CLAUDE.md configuration, and team scale.
A three-part series on Model Context Protocol. What MCP is, how to connect Claude to your tools, and the enterprise workflow that actually closes the loop.
Claude Code is not an autocomplete tool with a chat interface. It is an agentic loop that reads files, runs commands, edits code, and calls other agents — until the task is done. Understanding the architecture changes how you use it.
→ Methodology"It works but I don't know why" is a ticking time bomb. Here is why vibe coding destroys codebases — and how you avoid the trap.
→ MethodologyThe practical workflow that separates productive AI-assisted development from wasted tokens. Read the project. Plan on disk. Flush the context. Build from the plan. Review every line. This is not a suggestion — it is the process.
→ ProductionAI makes the first 10% of any initiative effortless. The 90% that delivers real value requires existing expertise to even know what needs building. The demo trap isn't a developer problem — it's an organizational pattern.
→A full-day workshop for the entire development team. You leave with working configuration, a shared methodology and code you actually checked in during the workshop.
Duration: 8 hours