Red Hat Ansible Lightspeed with IBM watsonx Code Assistant is a generative AI service designed for Ansible platform engineers and developers. It uses natural-language prompts to produce code recommendations based on Ansible best practices, helping teams create, maintain, and scale automation content more efficiently across various domains.

Key Features

Generative AI for Ansible Leverages IBM watsonx foundation models to generate Ansible code

• Accepts natural-language prompts from users
• Produces code recommendations built on Ansible best practices

Full Ansible Playbook Generation Creates complete Ansible content from a single prompt

• Generates single tasks, multiple tasks, or entire Ansible Playbooks
• Accelerates automation adoption across teams

Code Maintenance and Quality Helps teams maintain and improve their automation codebase

• Ansible code bot scans existing content for updates
• Provides recommendations to maintain quality and consistency

Model Customization Allows tailoring of the AI model to organizational needs

• Uses existing Ansible content to improve code quality and accuracy
• Refines recommendations as the content repository grows

Flexible Deployment Offers various deployment options to suit different needs

• Available for on-premise or cloud deployment
• Supports organizations with data privacy requirements or air-gapped environments

Benefits

Enhanced Productivity Streamlines the creation of Ansible content

• Faster and more accurate code generation
• Directly integrated into code editing environments via Ansible VS Code extension

Expanded Automation Adoption Empowers more team members to create automated content

• Converts subject matter expertise into reliable Ansible code
• Fosters trust in AI-generated code through content source matching

Improved Code Quality Ensures adherence to Ansible best practices

• Post-processing capabilities maintain code standards
• Automatic update recommendations for existing codebases

Customized Recommendations Tailors output to specific organizational needs

• Improves relevance of code suggestions over time
• Adapts to unique automation patterns and requirements