The Core Execution Loop

The Reality of AI-Assisted Development

Most developers approach AI coding tools as "faster typing." They prompt for code, copy-paste results, and hope for the best.

This approach hits a ceiling quickly:

• Context gets lost between sessions
• The AI makes the same mistakes repeatedly
• Quality varies wildly
• Decisions get re-litigated

The alternative isn't "coding faster." It's building a system that reliably produces outcomes.

The Four Pillars

The execution loop that enabled shipping ten production systems in 30 days consists of four interconnected practices:

┌─────────────────────────────────────────────────────┐
│                                                     │
│   1. Spec-Driven Development                        │
│      Explicit behavioral and architectural          │
│      contracts before any code                      │
│                        ↓                            │
│   2. Long-Lived System Documents                    │
│      Context the AI can reliably reference          │
│      across sessions                                │
│                        ↓                            │
│   3. Context Preservation Tools                     │
│      Custom MCPs, RAGs, and Skills that             │
│      maintain domain knowledge                      │
│                        ↓                            │
│   4. Agentic Workflows                              │
│      AI handles implementation while you            │
│      focus on design and correctness                │
│                                                     │
└─────────────────────────────────────────────────────┘

Each pillar reinforces the others. Remove one and the system degrades.

Pillar 1: Spec-Driven Development

Before writing code, create explicit contracts:

Behavioral Specs: What should happen?

• User stories with acceptance criteria
• Edge cases and error handling
• Expected inputs and outputs

Architectural Specs: How should it be built?

• Component boundaries
• Data flow
• Integration points
• Non-functional requirements

Example Spec Structure

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# Feature: User Authentication

## Behavioral Requirements
- Users can sign up with email/password
- Users can sign in with Google OAuth
- Failed login attempts are rate-limited (5 per minute)
- Sessions expire after 7 days of inactivity

## Acceptance Criteria
- [ ] Signup form validates email format
- [ ] Password requires minimum 8 characters
- [ ] OAuth redirect properly returns to original page
- [ ] Rate limiting returns 429 with retry-after header

## Architectural Notes
- Use existing auth service in /src/services/auth
- Store sessions in Redis with prefix "session:"
- JWT tokens for API, cookies for web

Pillar 2: Long-Lived System Documents

Context that persists across sessions:

AGENTS.md: Project-wide instructions for the AI

• Architecture overview
• Coding conventions
• DO and DON'T rules
• Common patterns

Domain Documentation: Business context

• What the product does
• Why decisions were made
• User personas and use cases

Technical Decision Records: Why we built it this way

• Options considered
• Trade-offs evaluated
• Final decision and rationale

Pillar 3: Context Preservation Tools

When projects grow beyond what fits in context:

MCP Servers: Connect the AI to your systems

• Database queries (read-only production data)
• API documentation
• Internal tools

RAG Systems: Searchable knowledge bases

• Large documentation sets
• Historical conversations
• Code analysis results

Skills/Prompts: Reusable task templates

• Code review checklist
• Migration procedures
• Deployment workflows

Pillar 4: Agentic Workflows

Let the AI handle implementation:

Your role shifts to:

• System design
• Defining intent
• Verifying correctness
• Architectural decisions

The AI handles:

• Writing code
• Running tests
• Fixing linting errors
• Documentation updates

The Compound Effect

When all four pillars work together:

Speed becomes a side effect. The real gain is consistency.

Getting Started

You don't need to implement everything at once:

Week 1: Start with specs

• Write a spec before your next feature
• Notice how it changes the AI's output

Week 2: Add AGENTS.md

• Document your project's patterns
• Add rules as you discover them

Week 3: Preserve decisions

• Start logging why, not just what
• Reference these in future sessions

Week 4: Evaluate tooling

• What context are you repeatedly re-explaining?
• What could be automated?

Common Objections

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Next: Deep dive into spec-driven development.