Webinar Description
Key Takeaways
- Explores the AI Development Lifecycle (AI-DLC) framework for integrating AI into software development workflows
- Addresses governance, traceability, and compliance challenges specific to AI-generated code and assets
- Features a case study from the Perforce P4 development team on using agent orchestrators to coordinate specialized AI agents
- Targets engineering leaders, DevOps professionals, and teams in VFX, animation, game development, and enterprise software
- Includes a preview of P4 Signals for tracking productivity, DORA metrics, and AI efficiency indicators
About the Event
This virtual webinar examines how organizations can build AI-integrated development workflows that maintain human oversight while enforcing process governance. As AI-generated code, content, and assets become standard in software development, teams face new challenges around quality control, traceability, and measuring the actual productivity impact of AI adoption. The session provides practical guidance on constructing workflows that balance AI capabilities with accountability requirements.
AI Development Lifecycle Framework
The webinar introduces the AI Development Lifecycle (AI-DLC) concept, a framework for managing AI integration across software development processes. Key areas of focus include establishing governance structures for AI-generated work, implementing code review processes that account for AI contributions, and creating traceability mechanisms that track AI involvement throughout the development pipeline. The framework emphasizes human-in-the-loop strategies that allow teams to scale AI usage without sacrificing oversight.
Perforce P4 Team Case Study
Presenters from Perforce share lessons learned from the P4 development team’s implementation of AI workflows. The team deployed an agent orchestrator to coordinate specialized AI agents, resulting in faster release cycles and increased output. This case study offers a concrete example of AI-DLC principles applied in a production environment, demonstrating how version control practices adapt when AI becomes a significant contributor to the codebase.
Productivity and Performance Measurement
A significant portion of the session addresses how engineering leaders can measure and demonstrate the value of AI investments. The webinar covers productivity metrics, DORA insights, and AI-specific efficiency indicators. Attendees will receive a preview of P4 Signals, a tool designed to help engineering leaders track these metrics and assess whether AI adoption is delivering proportional value.
Who Should Attend
The webinar is designed for engineering leaders, infrastructure leads, software developers, and product managers responsible for AI adoption strategies. Teams working in VFX, animation, game development, and enterprise software will find the content particularly relevant, as will organizations using version control systems and DevOps workflows that must accommodate increasing volumes of AI-generated contributions.