If you’ve used AI coding assistants, you know the pattern: start a conversation, get something decent, then spend the next 3 hours debugging hallucinations and stitching fragments together.
I got tired of that cycle. So I built a system.
The Problem With Chat-Based AI Development
AI assistants are brilliant at generating code. They’re terrible at maintaining context across a full project. Every conversation starts fresh. Every session forgets what you decided yesterday. The output is good in isolation and chaotic in aggregate.
The solution wasn’t to use AI less. It was to orchestrate it.
The 9-Stage Pipeline
Here’s the system that now powers every project I build:
- Analyze — AI reads the codebase, maps dependencies, identifies patterns
- Plan — Generate a structured plan with task dependencies
- Parallel — Parse the plan, identify independent tasks, queue them for parallel execution
- Implement — Execute tasks (multiple agents can run simultaneously)
- Security — Automated security scan of all changes
- Test — Run test suites, validate output
- Cleanup — Remove dead code, fix formatting, optimize
- Review — Full code review against the original plan
- Complete — Final validation and session summary
The Secret: CLAUDE.md
The breakthrough was putting the entire system into a single markdown file that the AI reads at the start of every session. It contains:
- Pipeline stage definitions and transitions
- Task queue format and dependency rules
- Session management (lock files, heartbeats)
- Error handling procedures
- Edge case documentation
The AI doesn’t need to remember the system. It reads the system every time.
Parallel Execution
The real power unlocked when I added parallel task execution. A Python script (parse_plan.py) analyzes the plan, identifies tasks with no unresolved dependencies, and queues them for simultaneous execution.
Instead of one AI agent doing everything sequentially, multiple agents work on independent tasks at the same time. A backend agent builds the API while a frontend agent builds the UI. A test agent writes tests while a security agent scans for vulnerabilities.
Results
This very website — the one you’re reading right now — was built using this system. Every page, every component, every animation was generated through the pipeline.
The difference:
- Before: 3-hour AI sessions producing inconsistent output
- After: Structured builds that ship production-grade code in a single session
The Template Is Free
I packaged the entire orchestrator as a downloadable template. CLAUDE.md, the pipeline stages, the Python tools, the task queue format — all of it.
Check the Resources page if you want to try it yourself.
Originally posted on LinkedIn.