AI Agents Across the Software Lifecycle

Two problems have always been hard in software: understanding what's actually happening across a distributed system, and keeping quality high from commit to production. Both are about context—too much of it, spread across too many services, teams, and moving parts. AI agents are making both tractable. This month, two practitioners show how they're putting that to work in production.

What We'll Explore
- Using AI agents to measure and improve performance across distributed systems—where no single team has end-to-end visibility
- AI agents in the software delivery pipeline: improving both speed and quality
- Where agents excel: maintaining context across complexity that humans can't hold in their heads
- Practical trade-offs: what to hand off to agents, what to keep human
- Open discussion: your use cases across the software lifecycle

Speakers
Two speakers TBD — We're lining up two practitioners for this one:
- Someone using AI agents to understand and improve a complex distributed enterprise system
- Someone using AI agents in the software delivery pipeline to improve production and quality

Details to follow when confirmed.

Who Should Come
If you work in software—engineering, platform, DevOps, architecture, or leadership—and you're curious where AI agents actually fit in the day-to-day work of building and running systems, this is for you. We're not talking about building agents from scratch. We're talking about using them to get real work done.

Bring your curiosity and your beverage of choice.