ai-development-patterns
A curated collection of 24 practical patterns for integrating AI into software development workflows, organized by maturity level and lifecycle phase. Provides guidance on readiness assessment, security, specification-driven development, and operational controls for AI-assisted coding.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | PaulDuvall/ai-development-patterns |
| Owner | PaulDuvall |
| Primary language | Python |
| License | MIT — OSI-approved |
| Stars | 603 |
| Forks | 48 |
| Open issues | 1 |
| Latest release | Unknown |
| Last updated | 2026-07-05 |
| Source | https://github.com/PaulDuvall/ai-development-patterns |
What ai-development-patterns is
Pattern catalog emphasizing Harness Engineering principles: feedforward controls (codified rules, specs, planned implementation) and feedback mechanisms (observable development, adversarial evaluation, error resolution) across foundation, development, and operations lifecycle phases. Includes automated verification of pattern adoption claims via CI pipeline.
Get the ai-development-patterns source
Clone the repository and explore it locally.
git clone https://github.com/PaulDuvall/ai-development-patterns.gitcd ai-development-patterns# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Patterns are presented as a dependency graph; adoption order matters. Foundation patterns (Readiness Assessment, Codified Rules, Security Sandbox) should precede Development patterns to establish baseline governance.
- Implementation requires translation to your specific tech stack, CI/CD platform, and AI tools. Use the patterns as a checklist, not a copy-paste solution.
- The catalog emphasizes feedforward (pre-action guides) and feedback (post-action sensors) as complementary; implementing only one category risks either brittleness or repeated failures.
- Verify that your team's AI tooling (models, IDEs, platforms) supports the workflow controls you plan to add—especially for rules propagation and autonomous remediation patterns.
- Patterns are subject to evolution as the field matures; treat the repository as a living reference, not a static standard.
When to avoid it — and what to weigh
- Seeking a Production Framework or Library — This is a patterns reference, not executable code or a framework. It provides strategic guidance but requires custom implementation for your tech stack.
- Looking for Benchmarks or Vendor Comparisons — The catalog does not include performance benchmarks, tool evaluations, or comparisons between AI platforms (e.g., GitHub Copilot vs. Claude vs. local models).
- Needing Specific Integration Instructions — Examples are provided but are illustrative only. Integration with CI/CD systems, AI platforms, or monitoring tools requires domain-specific customization not detailed in the repository.
- Expecting Automated Compliance or Security Auditing — While the catalog includes security-related patterns (Security Sandbox, Security Orchestration), implementing these requires active engineering work; the patterns are not automated enforcement tools.
License & commercial use
MIT License. Permissive OSI-approved license allowing commercial use, modification, and distribution with minimal restrictions. Requires retention of copyright and license notice.
MIT license permits commercial use of the patterns catalog itself without warranty or liability restrictions. However, patterns describe best practices and governance models; commercial implementation and support require your own engineering effort or external consultation.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
The catalog addresses security as a first-class concern: Security Sandbox (isolate AI model execution), Security Orchestration (centralized enforcement), and Policy Generation (consistent guardrails) are Foundation-level patterns. However, security is a design concern, not a guarantee. Implementation requires threat modeling for your specific tools and workflows. No security audit or penetration testing data provided.
Alternatives to consider
GitHub Copilot Enterprise governance docs
Provides vendor-specific guidance for one widely-used AI assistant; narrower scope but more prescriptive for that tool.
Martin Fowler's Harness Engineering article + homegrown patterns
Original source for the Harness Engineering lens used here; if you only need the conceptual framework, reading that directly may be more efficient than implementing the full catalog.
If your organization has already defined bespoke AI governance, this catalog may be redundant. Use it instead for gaps or validation of existing practices.
Build on ai-development-patterns with DEV.co software developers
Start with the free AI Development Readiness Scorecard to assess your maturity, then use this catalog to plan pattern adoption. Consult with engineers experienced in AI governance to customize implementation for your org.
Talk to DEV.coRelated open-source tools
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ai-development-patterns FAQ
Do I need to implement all 24 patterns?
Are there working code examples or templates?
How are adoption claims verified?
Does this work with my AI tool (Copilot, Claude, open-source model)?
Work with a software development agency
DEV.co helps companies turn open-source tools like ai-development-patterns into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your open-source devops stack.
Ready to bring AI-assisted development into your team?
Start with the free AI Development Readiness Scorecard to assess your maturity, then use this catalog to plan pattern adoption. Consult with engineers experienced in AI governance to customize implementation for your org.