From prompts to workflows with AI

AI coding tools can be impressive in simple demos. Ask a question, generate some code, and move on.

But real development work gets messier.

Larger tasks require more context. Tools need access to your codebase. Agents can take action on your behalf. New concepts like memory, context windows, MCP servers, and multi-step workflows are showing up fast — and it’s not always clear how these pieces fit together in practice.

Join us for a practical look at how modern AI development workflows actually work once you move beyond simple prompting.

We’ll break down the key concepts behind today’s AI tools, show where they help in real development work, and discuss where human oversight still matters.

What you'll learn:

• Why simple prompt-to-code workflows often break down

• How agents, memory, and context shape modern AI workflows

• How tools and integrations expand what AI can do
  

• Common limitations and failure modes to watch for
  

• What effective AI workflows look like in real development teams