Modern AI Workflow Patterns

What you'll learn

• Three battle-tested workflow patterns that turn AI coding agents from unpredictable interns into reliable collaborators
• Each lesson is a self-contained pattern you can apply today: one-page specs, alignment interviews, and autonomous subagent workflows
• No multi-week certification here. Pick the pattern that matches your problem and ship faster tonight

You've heard the pitch: AI coding agents will 10x your output. What the pitch leaves out is that the agent's output is only as good as the instructions you give it. Hand an agent a vague request and it builds the wrong thing. Let it run unchecked and it rewrites half your codebase chasing a phantom edge case. Treat it like a search engine and it gives you confident-sounding answers with no way to verify them.

The difference between builders who get real value from AI agents and those who abandon them after two frustrating sessions is workflow. The agent itself is rarely the problem. The problem is what happens around the agent: how you define the task, how you validate the output, and how you divide work between yourself and the machine.

This course covers three patterns, each one a complete workflow you can adapt to your own projects. They work across Claude Code, Codex, Cursor, and any agent that reads files and runs commands. They don't depend on a specific model version or a vendor's latest feature release. They're about process, not product.

Prerequisites

• Claude Code installed (claude doctor passes). The examples use Claude Code but the patterns transfer to any terminal-based coding agent.
• Basic terminal comfort: you can cd, ls, mkdir, and edit text files.
• A project to work on. These patterns are useless in a vacuum. Bring a real repo, even a small one.
• 5-10 minutes per lesson to read and understand the pattern, then as long as your actual build takes.

Course structure

1. Spec-Driven Development turns a one-page markdown spec into working code without the agent going off-track. You'll learn how to write specs that agents can actually follow, how to pin context so the agent doesn't forget what it's building halfway through, and how to set constraint boundaries that keep scope creep in check. Includes real prompt templates.

2. The Alignment Interview uses interactive subagents to stress-test project ideas before you write a line of code. Instead of discovering architectural problems three hours into a build, you resolve edge cases early by having an agent play the role of a skeptical reviewer. Includes the exact interview prompt you can copy.

3. Autonomous Subagent Workflows teaches you to delegate research, linting, and background tasks to subagents while you focus on architecture and design decisions. The subagent does the scutwork; you stay in the driver's seat. Practical patterns that work today, not a theoretical manifesto on multi-agent systems.

Final build

There is no final build project in this course because the patterns are the project. By the end of the three lessons, you'll have a set of Claude Code agents and markdown templates you can drop into any repo. The final build is your own workflow: a CLAUDE.md that encodes your project conventions, a spec template that keeps agents on track, an alignment interview agent that stress-tests ideas, and a fleet of subagents handling background work.

After you've applied all three patterns to one project, you'll have a workflow that catches bad ideas before they become bad code, ships working features from one-page specs, and handles the boring parts while you stay focused on what matters.

New to AI coding agents? Start with Spec-Driven Development. Already comfortable with Claude Code but tired of re-explaining your project? Jump to Autonomous Subagent Workflows. Building something new and want to derisk it before day one of coding? The Alignment Interview is your starting point.