Patterns & Best Practices

Distilled wisdom from the AI coding community

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Core Patterns in Agent-First Development

Through analyzing 47 posts and thousands of developer experiences, clear patterns have emerged that separate successful AI-assisted development from frustrating failures. These patterns, discovered through collective experimentation, form the foundation of effective agent-first workflows.

The Four Pillars of Success

1. Context Architecture

Pattern: Successful developers treat context as infrastructure, not an afterthought.

The Context Pyramid

flowchart TB
    subgraph "Strategic Context"
        A[Project Vision & Goals]
        A1[Business Requirements]
        A2[Success Metrics]
        A --> A1
        A --> A2
    end
    
    subgraph "System Context"
        B[Architecture & Design]
        B1[Tech Stack Decisions]
        B2[API Contracts]
        B3[Database Schema]
        B --> B1
        B --> B2
        B --> B3
    end
    
    subgraph "Module Context"
        C[Component Summaries]
        C1[ai_module_summary.md]
        C2[Dependencies Map]
        C3[Test Coverage]
        C --> C1
        C --> C2
        C --> C3
    end
    
    subgraph "Task Context"
        D[Current Workplan]
        D1[Task Objectives]
        D2[Acceptance Criteria]
        D3[Edge Cases]
        D --> D1
        D --> D2
        D --> D3
    end
    
    subgraph "Code Context"
        E[Implementation Details]
        E1[Similar Examples]
        E2[.cursorrules]
        E3[Recent Changes]
        E --> E1
        E --> E2
        E --> E3
    end
    
    A -.->|Informs| B
    B -.->|Guides| C
    C -.->|Shapes| D
    D -.->|Directs| E
    
    style A fill:#e3f2fd
    style B fill:#bbdefb
    style C fill:#90caf9
    style D fill:#64b5f6
    style E fill:#42a5f5
    
    style A1 fill:#e3f2fd
    style A2 fill:#e3f2fd
    style B1 fill:#bbdefb
    style B2 fill:#bbdefb
    style B3 fill:#bbdefb
    style C1 fill:#90caf9
    style C2 fill:#90caf9
    style C3 fill:#90caf9
    style D1 fill:#64b5f6
    style D2 fill:#64b5f6
    style D3 fill:#64b5f6
    style E1 fill:#42a5f5
    style E2 fill:#42a5f5
    style E3 fill:#42a5f5

Implementation:

  • Project Level: Maintain a clear README and architecture document
  • Module Level: Create ai_module_summary.md files for each component
  • Task Level: Use detailed workplans that preserve decision rationale
  • Code Level: Reference similar existing code as examples

2. Task Decomposition

Pattern: Break complex features into atomic, verifiable steps.

Tip

The Goldilocks Principle: Tasks should be “just right” - not so large that AI loses focus, not so small that overhead exceeds value.

Ideal task size: 15-30 minutes of implementation time

Examples of well-scoped tasks:

  • ✅ “Add email validation to the signup form”
  • ✅ “Create a reusable Button component with hover states”
  • ❌ “Build the entire authentication system”
  • ❌ “Fix all the bugs”

3. Progressive Enhancement

Pattern: Start with a working skeleton and iteratively add features.

flowchart LR
    subgraph "1. Foundation"
        A[Basic Structure]
        A1[File Setup]
        A2[Type Definitions]
        A3[Minimal UI]
    end
    
    subgraph "2. Core"
        B[Core Functionality]
        B1[Happy Path]
        B2[Basic Tests]
        B3[API Integration]
    end
    
    subgraph "3. Robustness"
        C[Edge Cases]
        C1[Error Handling]
        C2[Validation]
        C3[Loading States]
    end
    
    subgraph "4. Optimization"
        D[Performance]
        D1[Caching]
        D2[Lazy Loading]
        D3[Bundle Size]
    end
    
    subgraph "5. Refinement"
        E[Polish]
        E1[Animations]
        E2[Accessibility]
        E3[Documentation]
    end
    
    A --> B
    B --> C
    C --> D
    D --> E
    
    A --> A1
    A --> A2
    A --> A3
    
    B --> B1
    B --> B2
    B --> B3
    
    C --> C1
    C --> C2
    C --> C3
    
    D --> D1
    D --> D2
    D --> D3
    
    E --> E1
    E --> E2
    E --> E3
    
    style A fill:#e8f5e9
    style B fill:#c8e6c9
    style C fill:#a5d6a7
    style D fill:#81c784
    style E fill:#66bb6a
    
    style A1 fill:#f1f8e9
    style A2 fill:#f1f8e9
    style A3 fill:#f1f8e9
    
    style B1 fill:#dcedc8
    style B2 fill:#dcedc8
    style B3 fill:#dcedc8
    
    style C1 fill:#c5e1a5
    style C2 fill:#c5e1a5
    style C3 fill:#c5e1a5
    
    style D1 fill:#aed581
    style D2 fill:#aed581
    style D3 fill:#aed581
    
    style E1 fill:#9ccc65
    style E2 fill:#9ccc65
    style E3 fill:#9ccc65

This approach:

  • Provides immediate feedback
  • Maintains a working state
  • Allows for course correction
  • Builds AI understanding incrementally

4. Explicit Constraints

Pattern: Tell AI what NOT to do as clearly as what TO do.

Common constraint patterns:

## Constraints for this task:
- Do NOT modify any existing tests
- Do NOT change the database schema
- Do NOT refactor unrelated code
- ONLY work on the files mentioned above
- PRESERVE all existing functionality

Workflow Patterns by Project Phase

Starting New Projects

The “Vision-First” Pattern

  1. Define the End State: Start with user stories and success criteria
  2. Design the Interface: Use AI to create mockups/wireframes first
  3. Plan the Architecture: Let AI suggest structure based on requirements
  4. Implement Vertically: Build one complete feature slice at a time

Working with Existing Codebases

The “Archaeological Dig” Pattern

  1. Map the Territory: Have AI analyze and document existing structure
  2. Establish Beachheads: Start with isolated, low-risk changes
  3. Build Understanding: Create tests for existing functionality
  4. Expand Gradually: Work outward from well-understood areas

Debugging and Troubleshooting

The “Forensic Analysis” Pattern

  1. Reproduce First: Always start with a failing test case
  2. Isolate the Problem: Binary search through code changes
  3. Hypothesize and Test: Let AI suggest potential causes
  4. Verify the Fix: Ensure the solution doesn’t break other features

Anti-Patterns to Avoid

1. The “Magic Prompt” Fallacy

❌ Anti-pattern: Believing one perfect prompt will generate entire applications

✅ Better approach: Use iterative refinement with clear checkpoints

2. Context Overload

❌ Anti-pattern: Dumping entire codebases into AI context

✅ Better approach: Curate relevant context for each specific task

3. The “Yes Man” Trap

❌ Anti-pattern: Accepting AI suggestions without review

✅ Better approach: Maintain healthy skepticism and verify all changes

4. Scope Creep Enablement

❌ Anti-pattern: Letting AI “improve” code beyond the asked task

✅ Better approach: Use explicit constraints and focused prompts

Advanced Patterns

Multi-Agent Orchestration

The community has discovered that different AI models excel at different tasks:

Agent Role Best For Example Models
Architect High-level planning, system design Claude 3.5, GPT-4
Developer Implementation, coding Cursor Tab, Copilot
Reviewer Security analysis, code review Specialized models
Documenter Creating docs, comments GPT-3.5, Claude Instant

Context Preservation Strategies

Pattern: The “Memory Palace” Approach

  1. Hierarchical Documentation

    project-root/
├── AI_CONTEXT.md          # Project overview
├── docs/
│   ├── architecture.md    # System design
│   └── decisions.md       # ADRs
└── src/
    ├── module1/
    │   └── AI_SUMMARY.md  # Module context
    └── module2/
        └── AI_SUMMARY.md  # Module context

  2. Git-Anchored Context

    • Tag significant milestones
    • Reference commits in workplans
    • Use branches for experimental AI work

  3. Session Handovers

    • Create handover documents when switching between AI sessions
    • Include: current state, recent changes, next steps
    • Reference specific file versions

Metrics for Success

The community has identified key indicators of healthy AI-assisted development:

Velocity Metrics

  • Task Completion Rate: 80%+ of AI attempts should succeed first try
  • Iteration Count: Average 1-3 iterations per feature
  • Context Switches: Minimize by batching related work

Quality Metrics

  • Test Coverage: AI-generated code should include tests
  • Review Changes: <20% of AI code should need significant revision
  • Bug Introduction Rate: Should be lower than manual coding

Process Metrics

  • Context Preparation Time: 10-20% of total development time
  • Prompt Refinement: 2-3 iterations to get desired output
  • Documentation Ratio: 1:4 (docs to code)

The Path Forward

The patterns discovered by the community point toward a future where:

  1. Context becomes programmable infrastructure
  2. AI agents specialize and collaborate like human teams
  3. Development workflows adapt dynamically to project needs
  4. Quality emerges from process, not just review

These patterns aren’t just optimizations - they represent a fundamental shift in how we conceptualize software development. By embracing these practices, developers can achieve what the community calls “flow state at scale” - the ability to maintain high productivity across complex projects.

These patterns emerge from analyzing 466.7% growth in community engagement around AI-assisted development. Explore the raw insights →