AI-Native Software Engineering: Enduring Principles, New Pace
AI is rapidly
changing how software is produced, but not the need to engineer software to
meet business and mission goals. AI is enabling developers to move from idea to
implementation at incredible speeds. However, a fast pace has implications that
teams must manage. Product quality does not come for free, and there is some
tendency for AI to accelerate the accumulation of technical debt. In addition,
what works well on small examples doesn't always work as well with large code
bases. So, what's a good software engineer to do?
Software
engineering principles and practices are essential in guiding the use of AI
towards production-ready outcomes. In this webcast, experienced software
engineers discuss observations and lessons from their application of AI-native
software engineering and study of its use across multiple projects.
What Will
Attendees Learn?
- distinguish between “vibe coding” and software engineering
- understand how software engineering principles improve use of AI and where they need to be adapted to be used with AI
- recognize different criteria against which to assess benefits of AI-native software engineering (e.g., productivity and software quality) and their potential tradeoffs
Who Should
Attend?
- AI Developers, vibe coders, and software engineers at all experience levels
- Architects, business analysts, quality assurance engineers
- Technical leads and engineering managers
Speaker Details
Scott Sinclair
Software Architect at the Software Engineering Institute
(SEI) with over 20 years of experience across all aspects of the software
design lifecycle. He supports multiple organizations, advancing architecture
practices and delivering practical solutions. He also teaches SEI courses on
software architecture design, analysis and documentation. His current work
includes modernizing legacy software using AI, alongside traditional
modernization activities.
James Ivers
Principal Engineer and lead of the AI Workflows and
Architecture Modernization group at the Software Engineering Institute. His
experience spans 30 years of research and application of work in software
architecture, code analysis, formal methods, and scaling our ability to evolve
software. He is a co-author of the book Documenting Software
Architectures. His most recent work
focuses on using AI for software engineering to support large-scale software
modernization.
Mario Benitez
Software Architect at the Software Engineering
Institute, where he works across organizations, from architecting large-scale
systems to advancing software architecture practices. With over 20 years of
experience in software engineering, he has built and delivered complex,
high-reliability systems, including those in safety-critical environments. He
currently focuses on the practical application of AI to enhance large-scale
software modernization, enabling organizations to transform complex systems
more efficiently and effectively.
Event Topic
Artificial Intelligence, Machine Learning, Digital TransformationRelevant Audiences
All Military, All Federal Government