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Agentic Engineering

AI Coding Trends & Patterns

A collection of emerging patterns, techniques, and methodologies in AI-assisted software development. These approaches represent evolving best practices from the community.

Development Patterns

Section titled “Development Patterns”

Ralph Wiggum

Section titled “Ralph Wiggum”

A technique for running AI coding agents in continuous loops where the AI iterates on its own output repeatedly until tests pass and the code compiles. Uses “stop hooks” to prevent premature exit, forcing the AI to refine its work through multiple passes instead of attempting perfection on the first try.

Key characteristics:

  • Deterministically bad failures (predictable and informative)
  • Automatic retry logic
  • Loop continues until completion criteria met
  • Success depends on good prompt engineering

Use cases:

  • Refactoring loops (duplicate code detection and cleanup)
  • Linting loops (incremental error fixing)
  • Entropy reduction (code smell removal)

Resources:

Spec-driven Development (spec-kit)

Section titled “Spec-driven Development (spec-kit)”

Intent-driven development where specifications define the “what” before the “how” using guardrails and organizational principles. Multi-step refinement rather than one-shot code generation from prompts, relying on advanced AI model capabilities for specification interpretation.

Key characteristics:

  • Specifications defined upfront
  • Structured process with guardrails
  • Multi-step refinement
  • Works with GitHub Copilot, Claude Code, Gemini CLI

Benefits:

  • Clearer requirements before implementation
  • Better alignment with business goals
  • More maintainable code
  • Reduced back-and-forth iterations

Resources:

Research, Plan, Implement (RPI)

Section titled “Research, Plan, Implement (RPI)”

A three-phase workflow where AI first researches the codebase and requirements, then creates a phased implementation plan with atomic tasks, and finally implements the solution. Emphasizes research before planning to avoid false assumptions.

The three phases:

  1. Research: AI explores the codebase, understands patterns, discovers constraints
  2. Plan: Create phased approach with atomic tasks (markdown checkboxes)
  3. Implement: Execute the plan with context from research

Key principle: Planning without research leads to bad assumptions. RPI begins with research to ground plans in reality.

Resources:

Prompting Patterns

Section titled “Prompting Patterns”

Stepwise / Iterative Prompting

Section titled “Stepwise / Iterative Prompting”

Breaking complex tasks into small, manageable chunks with feedback loops between each iteration, rather than requesting monolithic code blocks.

Benefits:

  • Easier to debug and validate
  • Better context management
  • More control over direction
  • Reduced cognitive load

Example approach:

  1. “First, update the type definitions”
  2. Review and approve
  3. “Now update the implementation to match”
  4. Review and approve
  5. “Finally, add tests”

Resources:

Context Packing / Brain Dumps

Section titled “Context Packing / Brain Dumps”

The practice of frontloading all relevant context (codebase architecture, API docs, constraints, invariants) into prompts before coding.

What to include:

  • Architecture overview
  • API documentation
  • Constraints and requirements
  • Existing patterns and conventions
  • Known gotchas or edge cases

Benefit: Reduces hallucinations and improves first-attempt accuracy.

Resources:

Chain-of-Thought Prompting

Section titled “Chain-of-Thought Prompting”

Asking AI to explain its reasoning step-by-step before providing code, similar to requiring a design doc.

Example prompt structure:

Before writing code, explain:
1. What problem you're solving
2. Your approach and why
3. Key design decisions
4. Potential trade-offs


Then provide the implementation.

Benefits:

  • Catches logical errors early
  • Makes reasoning auditable
  • Helps humans understand approach
  • Often improves code quality

Resources:

Development Styles

Section titled “Development Styles”

Vibe Coding / Prompt-First Development

Section titled “Vibe Coding / Prompt-First Development”

A style of AI-assisted development where developers describe what they want in natural language and iterate with the AI.

Characteristics:

  • Natural language specifications
  • Rapid iteration
  • Learn by doing
  • Less upfront planning

When it works:

  • Prototyping and exploration
  • Well-understood domains
  • Individual developer projects

Risks:

  • Accumulated technical debt
  • Unclear requirements
  • Harder to maintain long-term

Resources:

Objective-Validation Protocol

Section titled “Objective-Validation Protocol”

A systematic approach to defining clear success criteria and validation objectives for AI-generated code, establishing performance thresholds and tracking validation goals across iterations.

Components:

  • Clear success criteria
  • Performance thresholds
  • Validation checkpoints
  • Tracking across iterations

Benefits:

  • Measurable progress
  • Objective quality gates
  • Easier debugging
  • Better documentation

Adoption Considerations

Section titled “Adoption Considerations”

When evaluating these patterns:

  • Team maturity: Some patterns require more AI experience
  • Project phase: Different patterns suit exploration vs. production
  • Code criticality: Safety-critical code needs more rigorous approaches
  • Team size: Collaborative work may need more structured patterns

This is a living document. Patterns will evolve as the community learns what works.