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OPEN TALK: Move Faster and Break Fewer Things with Observability + AI


Richard Whitehead
Moogsoft, Chief Evangelist

Richard Whitehead serves as Chief Evangelist of Moogsoft. With more than two decades of industry experience, he has a keen sense of what is required to make a transformational solution a global success. Prior to Moogsoft, Richard was VP Strategic Technologies at Micromuse (MUSE/IBM) where he was responsible for identifying strategic markets, partnerships, and product research, and was instrumental in bringing Netcool (IBM Tivoli Netcool) to market.

Additionally, he re-invigorated the SMARTS product portfolio and was CTO at Clarus Systems. (RVBD). Richard served on Splunk Technology’s (SPLK) Advisory Board for seven years through their Series A, providing product and market guidance, and has served on the Advisory Boards of RedSeal and Mention Networks. Richard is also a charter member of the Telemanagement Forum (TMF) NGOSS architecture committee, Chaired the Telecommunications Working Group for the DMTF, and recently co-chaired the ONUG Monitoring & Analytics Working Group.


A key challenge when working with software is that it’s invisible. It does not inherently lend itself to the universal DevOps goal of “Telemetry Everywhere.” While engineers consciously code their product to emit metrics, logs and traces that allow them to observe the invisible, traditional monitoring methods fall short of generating meaningful data about incidents, leaving teams with excess toil when things break. This talk will explore the relationship between observability and SDLC practices which allow AI to lead the Ops side of DevOps, so developers and SREs can move faster, innovate more and operate less.

Attendees will learn:
- How introducing visibility and control over incidents earlier in the development cycle can reduce toil.
- How to leverage Service Level Objectives (SLOs), error budgets and the ‘wisdom of production’ to improve the Ops part of DevOps.
- Methods for using AI-driven observability to turn every incident into a learning opportunity.

Discover how AI-driven observability methods help improve practices from Site Reliability Engineering to Continuous Integration and Deployment, and supports the transition from project to product-centric ways of working.