From Chaos to Clarity: How AI Revolutionized OpenSearch's Release Ma...
summary
OpenSearch automated release system OSCAR cuts manual toil across 130–140 repos and 3,300+ contributors by integrating LLMs (via Bedrock), Jenkins, GitHub, metrics and DynamoDB into an orchestrator that achieves 80% reduction in workflow turnaround and 90% reduction in time to identify bugs. It blocks malicious PR code before Jenkins runs, auto-generates release notes saving a week (used for 3.3), and is evolving from a monolith to a multi-agent design with live Jenkins status and agentic search for natural-language metrics queries.
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chapters (17)
- 0:00AI in OpenSearch releases
- 1:06OpenSearch release complexity
- 5:20Manual release challenges
- 6:01AI automation with Oscar 1.0
- 8:49Oscar 1.0 bottlenecks
- 9:10AI issue triage management
- 11:40PR security checks with AI
- 13:25Generating release notes
- 15:11Oscar 1.0 to Oscar 2.0
- 16:37Multi-agent in Oscar 2.0
- 18:20Jenkins agent improvements
- 20:47Live status tracking
- 22:11Agentic search for metrics
- 23:57Security advisories in releases
- 24:59Demo of Oscar 2.0
- 33:33Future of AI release management
- 37:27Q&A
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