A continuous cycle of ideation, planning, execution, and refinement — all driven by issues, feedback loops, and a persistent memory layer.
The Big Picture
Working with AI isn't about asking a chatbot to write code. It's about orchestrating a self-improving system where AI agents brainstorm, plan, build, review, audit, and monitor — connected by issues as the connective tissue.
Here's the full cycle:
┌──────────────────────────────┐
│ 📡 MONITOR │
│ Production Signals │
└──────┬───────────────┬───────┘
insights ↓ ↑ ship
┌──────────────────┐ ┌───────────────────┐
│ 💡 BRAINSTORM │ │ ✅ REVIEW │
│ Ideate & Design │ ◄──┤ Quality Gates + │
└────────┬─────────┘ │ Code Review │
feasibility ↕ └────────┬──────────┘
┌────────────────────┐ ↑ fix loop
│ 📋 PLAN │ ┌────────────────────┐
│ Issues & DAG │ │ ⚡ EXECUTE │
└────────┬───────────┘ │ Parallel Agents │
↓ └────────┬───────────┘
┌────────────────────┐ ↑
│ ⚖️ TRIAGE │─────────────┘
│ Prioritize │
└────────────────────┘
↑ new issues
┌────────────────────┐
│ 🔍 AUDIT │
│ Deep Analysis │
└────────────────────┘
🧠 MEMORY (always active)
context · rules · lessons learned
The Essential Flow (Start Here)
If you're new to AI-driven development, here are the five core steps. Master these first, then layer on the advanced concepts below.
Step 1: Brainstorm — Design the Feature with AI
"I want to add something like this — help me design it."
Collaborate with AI to explore ideas, validate approaches, and shape the feature before any code is written. This is where you describe your intent in plain language and let AI help you think through edge cases, UX, and architecture.
Step 2: Plan — Create Issues & Split the Work
Create a parent issue for the feature. AI analyses your codebase and breaks it into linked sub-issues — each one a clear, scoped piece of work with acceptance criteria.
Feature: User Dashboard
├── Issue #101: API endpoint for user stats
├── Issue #102: Dashboard React component
├── Issue #103: Caching layer for stats
└── Issue #104: E2E tests for dashboard
Step 3: Execute — AI Agents Build Each Issue
Assign issues to AI agents. Each agent writes code, tests, and docs for its scoped sub-issue.
spawn team → issue: #101 (API endpoint)
spawn team → issue: #102 (Dashboard component)
Step 4: Review — Validate Tests & Code
Check all tests pass, review code quality. If something's wrong, loop back to Execute. Once everything is green, commit and create a PR that closes the related issues.
The feedback loop: Review → Execute → Review (repeat until all checks pass).
Step 5: Audit — System-Wide Check
Audit the entire system for security, performance, architecture, and best practices. Create prioritized issues for anything found — these feed back into Plan.
The feedback loop: Audit → Plan (new issues enter the next cycle).
The Full System (Level Up)
Once you're comfortable with the core loop, these additions make the system dramatically more powerful.
🆕 Triage: Prioritize Before You Execute
Between Plan and Execute, add a triage step. Not all issues are equal — weigh each by:
- Severity — Is this a security hole or a nice-to-have?
- Effort — Can an agent do this in minutes or hours?
- Business impact — Does this affect users or just internal code?
- Dependencies — What blocks what?
AI ranks and sequences the work. You approve. This prevents low-priority tasks from consuming agent time while critical bugs sit in the backlog.
⚡ Parallel Agent Orchestration
Your issues form a dependency graph (a DAG — directed acyclic graph). Instead of running them one by one:
- The orchestrator finds all leaf nodes (issues with no blockers)
- Assigns them to agents simultaneously
- As each completes, newly unblocked issues are dispatched
#101 (API) ──────────┐
├──→ #104 (E2E tests)
#102 (Component) ────┘
#103 (Cache) ← independent, runs in parallel
This dramatically reduces wall-clock time compared to sequential execution.
🚧 Automated Quality Gates
Before any human sees the code, automated checks must all pass:
| Gate | What It Checks |
|---|---|
| Linting | Code style, formatting |
| Type checking | Type safety (TypeScript strict) |
| Test suite | All unit + integration tests |
| Security scan | Known vulnerabilities, secrets |
| Coverage | Minimum threshold (e.g. ≥ 80%) |
This filters out ~80% of issues before review even begins. The human/AI review then focuses on logic and architecture rather than catching surface-level problems.
📡 Monitor: Close the Loop with Reality
After shipping, the system doesn't stop. Monitor:
- Error rates and crash reports
- Latency (P95, P99)
- User behaviour and feedback
- Performance regressions
Anomalies auto-generate issues with full context. Weekly insight digests surface patterns. This feeds back to Brainstorm, grounding the next cycle in real-world data instead of assumptions.
The feedback loop: Monitor → Brainstorm (production insights inform what to build next).
🧠 Project Memory: The Context Layer
This is the secret weapon. A persistent knowledge base that every phase reads from and writes to:
- Architectural decisions (ADRs)
- Coding conventions and patterns
- Past audit findings and how they were resolved
- Lessons learned and edge cases
- Team preferences and style guides
Every audit finding updates the memory. Every resolved bug enriches it. AI agents consult this before writing any code — so the same mistake is never made twice.
All Feedback Loops
The real power of this system is in the loops. Each one creates a self-correcting mechanism:
| Loop | Trigger | Effect |
|---|---|---|
| Review → Execute | Failed tests or code issues | Agents fix and resubmit with specific failure context |
| Audit → Triage → Plan | Security, performance, or architecture findings | New prioritized issues enter the next cycle |
| Monitor → Brainstorm | Production anomalies or user feedback | Real-world data grounds the next ideation cycle |
| Brainstorm ↔ Plan | Feasibility concerns during planning | Design gets rethought before any code is written |
Getting Started
You don't need to implement everything at once. Here's a progression:
Week 1: Start with the 5-step essential flow. Use issues as your connective tissue.
Week 2: Add quality gates (linting + tests as automated checks before review).
Month 1: Introduce parallel execution — let multiple agents work on independent issues.
Month 2: Add the monitor phase and start feeding production data back into brainstorm.
Ongoing: Build your project memory. Every cycle, it gets smarter.
The goal isn't to replace your judgment — it's to amplify it. You steer. AI executes. Issues connect everything. And the system gets better with every cycle.
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