The Agentic Engineer Workflow You Need In 2026
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I've shipped AI-generated code on real engineering teams at enterprises and startups, with full code reviews, production users, and accountability for every merge. Most agentic coding tutorials show toy demos that fall apart the moment you join a real team. This video walks through my actual workflow: four parallel Claude Code windows tuned to different effort levels, context window management, git worktrees, MCP versus Bash decisions, and the engineering patterns that keep AI code from breaking your team.
What You'll Learn
How to run 4 parallel Claude Code windows with mixed high, medium, and low effort levels
When to use /compact versus /clear to stop context window drift from ruining your session
Why most spec-driven development, BMAD, and prompt engineering trends won't survive 6 months
The Smell command pattern that pushes Claude Code to follow Clean Code and design patterns
When to use MCP servers, Bash tools, or skills to connect Claude Code to external services
The honest difference between AI coding at freelance, startup, and enterprise scale
How to use git worktrees for parallel AI coding without merge conflicts
The realistic productivity gains from AI coding (30 to 60 percent, not 5x) and what they look like in production
How to think about agentic engineering as a mindset, not just a tool stack
Timestamps
0:00 The Agentic Engineer Mindset
0:30 My 4 Parallel Claude Code Window Setup
2:30 Managing Context Window Drift
3:22 Why I Avoid Focusing On Spec-Driven Development and BMAD
5:04 The Smell Command for Clean Code Quality
7:28 Connecting Claude Code to GitHub CLI
8:01 MCP Servers vs Bash Tools
10:32 AI Coding at Freelance, Startup, and Enterprise Scale
12:58 Git Worktrees for Parallel AI Coding Streams
15:56 The Real AI Productivity Gains
17:01 Becoming an AI Native Engineer
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