Daniel Helfand explains how applying reusability principles to AI workflows in DevOps can help developers and teams avoid inefficiency. The article presents strategies to standardize, version, and catalog AI assets for greater organizational value.

Don’t Reinvent the Wheel: A Developer’s Guide to AI Reusability

By Daniel Helfand

Introduction

AI is transforming software engineering, but with rapid adoption comes the risk of duplicated effort and inefficiency. This article discusses the importance of reusing AI workflows in development teams and presents a structured approach to making AI assets reusable and discoverable.

The Problem With Current AI Development Practices

Many organizations encourage AI usage in daily workflows, but developers are often left to create AI workflows from scratch. This leads to wasted effort, inconsistencies, and barriers to wider AI adoption, especially when the same prompts or automation steps are repeatedly reimplemented across teams.

Statistics show strong industry recognition of the challenge: According to the upcoming 2025 GitLab DevSecOps Report, 85% of DevSecOps professionals believe “Agentic AI will be most successful when implemented in a platform engineering approach.”

Key Steps for Making AI Reusable

Identify Developer Workflows That Consume Time
- Focus on frequent, high-effort tasks (e.g., fixing failing CI/CD pipelines).
Provide Context and Permissions for AI Models
- Equip workflows with appropriate source code, logs, and access rights so AI can operate effectively.
Test and Refine AI Handling
- Experiment with model prompts and tools to iterate on workflow effectiveness.
Standardize Execution Environments
- Use CI/CD jobs or other automation to ensure repeatable, reliable workflow execution.
Version All Parts of the Workflow
- Track models, prompts, tool versions, hardware, and sample data as unified releases for reproducibility.

Promoting Discoverability and Reuse: Software Catalogs

A central theme is the need for cataloging AI workflows—making reusable assets findable and accessible, similar to software or API catalogs. This supports:

Easy onboarding for new developers
Consistent AI implementations
Community-driven improvement of workflows

Treating AI workflows as engineering assets fosters collaboration and accelerates effective adoption across teams.

Conclusion

By investing in frameworks and practices to make AI workflows reusable and discoverable, organizations can avoid redundant work, save time, cut costs, and deliver better value to customers. Principles like modularization, versioning, automation, and asset cataloging apply to AI just as they do to traditional software engineering.

This post appeared first on “DevOps Blog”. Read the entire article here