Build or buy? See where eng teams are landing
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.
The question isn't whether AI belongs in production anymore. Here's what engineers at AWS Summit NYC 2026 told us about how agents run your software, what guardrails they want, and how the pricing should work.
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.
Watch how Resolve AI investigates a service timeout from application logs through Kubernetes pods down to failing memory modules in a UCS blade - building a complete causation chain in 3 minutes. See the stark contrast between traditional multi-team incident response (4 teams, multiple tools, hours of coordination) and AI-native investigation that maps dependencies from app code to storage infrastructure without organizational handoffs. Learn why engineering silos slow incident response and how AI agents can reason across the entire production stack as one connected system.
