Building agents for on-call and incidents?
On Demand Event | Duration: 45 minutes | Originally aired: January 8, 2026
10x engineers are no longer a myth. At Coinbase, AI already sits in incident channels, reads graphs, checks deploy logs, and flags false alarms so humans can focus on the hard problems. Learn how their engineering teams leverage Resolve AI hundreds of times per week to keep shipping velocity high while maintaining world-class reliability.
For more about how Coinbase made investigation time 72% faster, read the Coinbase case study.
You'll walk away knowing how Coinbase uses AI today to actually run production systems, not just summarize tickets or act as a sidekick in an editor.
Join our engineering leads for "Behind the Build", a webinar series deep-dive into how we built agents that run software.
We benchmarked Claude Sonnet 4.6's adaptive thinking on production incident investigations. Sonnet 4.6 at medium effort came close to Opus 4.6 at a fraction of the cost.
A frontier model can produce a thousand coherent answers. Most enterprise work needs exactly one correct one, and closing that gap is not a bigger model. It is the agent architecture around it. Here are the six layers that turn open-ended capability into a defined outcome, and why production incidents are the hardest test of whether they work.
Announcing my new role at Resolve AI to lead marketing - joining a mission-driven team reimagining software operations with Agentic AI and building a category-defining company.
