AI is reshaping software engineering, but better code generation alone does not automatically create better engineering outcomes.
Engineering teams still lose time to fragmented context across tickets, documentation, incidents, internal conversations, and operational systems. And when AI only has visibility into the code layer, it misses the history, constraints, and context that engineers need to make sound decisions.
Join Glean and Nile for a practical conversation on how modern engineering organizations are thinking about the AI stack more holistically: pairing coding tools with a shared context layer that helps teams troubleshoot faster, reduce context switching, and make AI more useful in real workflows.
You’ll hear how Nile uses Glean as a context surface across systems to support engineering and operational workflows, including incident response, while also creating a stronger foundation for AI adoption more broadly across the business.
What you’ll learn
- Why AI for engineering falls short when it only focuses on code generation
- Why the strongest AI stacks combine coding tools with a shared context layer
- What engineering leaders should evaluate across code, tickets, docs, incidents, chat, and operational knowledge
- What it takes to make AI useful, trustworthy, and scalable across the entire organization
Attendees
