Uploaded July 2026 | Updated September 2026, 2 weeks ago
Most people use AI coding agents by giving them one giant prompt and letting them run wild. That might work for a tiny project, but it quickly falls apart when you are building real software.
In this video, I walk through my complete AI development workflow using agents, specifications, roadmaps, testing, pull requests, CI, security scanning, and independent code reviews. I also demonstrate the process on a real project, from the initial request through review, fixes, validation, and the final merge.
The model matters, but your workflow matters more. Manual gates, clear acceptance criteria, small pull requests, and proper testing are what turn AI-generated code into maintainable software.
What does your AI development workflow look like? Let me know what you would add or change.
Website Article: christitus.com/my-ai-workflow
Github Repo: github.com/ChrisTitusTech/titus-ai
00:00 My AI Workflow
00:47 Build Guardrails First
01:54 Agents And Skills
03:04 Reducing Token Usage
03:56 Writing Project Specs
05:03 Testing And Linting
06:26 Acceptance Criteria
06:49 Building A Roadmap
07:15 Real Project Demo
09:31 Review Before Merging
11:30 Security And Versions
12:44 Independent AI Reviews
15:27 Local Code Reviews
17:03 Fixing Review Feedback
19:41 Continuous Integration
22:04 Final Merge Gate
22:46 Slow Is Smooth
24:04 Models Won’t Save You
►► Digital Downloads ➜ cttstore.com
►► Patreon ➜ patreon.com/christitustech
►► Twitch ➜ twitch.tv/christitustech
►► Website and Guides ➜ christitus.com
Most people use AI coding agents by giving them one giant prompt and letting them run wild. That might work for a tiny project, but it quickly falls apart when you are building real software.
In this video, I walk through my complete AI development workflow using agents, specifications, roadmaps, testing, pull requests, CI, security scanning, and independent code reviews. I also demonstrate the process on a real project, from the initial request through review, fixes, validation, and the final merge.
The model matters, but your workflow matters more. Manual gates, clear acceptance criteria, small pull requests, and proper testing are what turn AI-generated code into maintainable software.
What does your AI development workflow look like? Let me know what you would add or change.
Website Article: christitus.com/my-ai-workflow
Github Repo: github.com/ChrisTitusTech/titus-ai
00:00 My AI Workflow
00:47 Build Guardrails First
01:54 Agents And Skills
03:04 Reducing Token Usage
03:56 Writing Project Specs
05:03 Testing And Linting
06:26 Acceptance Criteria
06:49 Building A Roadmap
07:15 Real Project Demo
09:31 Review Before Merging
11:30 Security And Versions
12:44 Independent AI Reviews
15:27 Local Code Reviews
17:03 Fixing Review Feedback
19:41 Continuous Integration
22:04 Final Merge Gate
22:46 Slow Is Smooth
24:04 Models Won’t Save You
►► Digital Downloads ➜ cttstore.com
►► Patreon ➜ patreon.com/christitustech
►► Twitch ➜ twitch.tv/christitustech
►► Website and Guides ➜ christitus.com
