Generative DevOps
summary
Execs report 84% pressure to adopt AI and expect big gains (93% expect faster deploys); developers already use codegen (81%) but see productivity gains of 10–50% offset by more debugging (67%) and security fixes (68%). Concrete tools and results: Appvance AI produces retrainable codeless UI tests, MIT Synthetic Data Vault and Tonic create synthetic data matching production statistics, Harness continuous verification auto-rolls back bad releases, and Dynatrace Davis AI provides predictive anomaly detection and agentic observability.
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chapters (13)
- 0:00Introduction and speaker intro
- 2:50AI adoption pressure and expectations
- 7:48AI in non-codegen SDLC areas
- 9:14Testing with AI and autonomous tools
- 13:29Synthetic data generation for testing
- 16:38AI in build process automation
- 18:26Deployment verification with AI
- 20:53Self-healing CI/CD pipelines
- 22:17AI for monitoring and anomaly detection
- 24:25Agentic observability tools
- 26:10Implementation risks and governance
- 33:21OutSystems generative approach
- 36:32Key takeaways and Q&A
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