From Alerts to Autonomy: Advancing the Maturity Curve with Agentic Managed Streaming Platforms

Double the number of jobs on a multi-tenant stream processing platform, and you'll more than double the alerts, false positives, and real incidents your team must triage. Signal and noise both grow super-linearly with scale, consuming an ever-larger scale of the platform team’s bandwidth. Applying AI at the right points can bring that support growth curve back toward linear.

At JPMorgan Chase, a small platform team supports thousands of user-submitted Flink jobs processing more than 100 billion events per month. In this talk, we share the three-stage agentic support maturity curve that made this possible, and how you can apply it to your own platform:

1. Amplify the signals that matter. Learn AI/ML techniques can enrich platform telemetry and filter out alert noise so real problems stand out.
2. Human-initiated, AI-assisted diagnosis and remediation. See how correlating logs, metrics, and historical metadata with point-in-time state provides the context an agent needs to help engineers find root causes and fixes faster.
3. Agent-initiated diagnosis and remediation. Discover how agents can resolve routine issues on their own, keep the right people informed, and bring in humans only when necessary.

You'll leave this session with a practical blueprint for making support scale with your platform usage instead of outrunning it.