Learn how always-on agents run our prod backlog
On Demand Event | Duration: 36 minutes | Originally aired: January 22, 2026
For Financial Services companies, managing spend, cost, and ROI is the basis of their business.
While the cost of running, maintaining, and managing the production systems that power their banking applications balloons, ROI has not kept up. Observability tools proliferate, code multiplies, and the team size to manage it all expands—without resulting in productivity, velocity, or economic gains.
AI is changing that, and this discussion explores how FinServ companies are deploying it across their stack.
Featured guests include:
To learn more about how Financial Services companies are making cost and productivity gains by moving beyond the IDE, read the AI ROI Playbook for Financial Services.
Join our engineering leads for "Behind the Build", a webinar series deep-dive into how we built agents that run software.
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.
Resolve AI, powered by advanced Agentic AI, has transformed how Blueground manages production engineering and software operations, seamlessly handling alerts, supporting root cause analysis, and alleviating the stress of on-call shifts.
Resolve AI now supports the Model Context Protocol (MCP) for Atlassian, enabling it to securely access your Jira tickets and Confluence runbooks. By combining this institutional knowledge with deep context of your code and infrastructure, Resolve AI can autonomously execute runbooks during incidents, optimize costs based on historical intent, and provide architectural guidance for new features.
