Artificial intelligence is transforming how organizations prepare for, respond to, and recover from critical events. As AI capabilities continue to evolve, leaders are exploring how to harness its potential while maintaining the human oversight essential for effective decision making. This session will examine the practical role of AI in modern incident response, separating hype from reality. Attendees will learn where AI can provide meaningful value, including decision support, threat analysis, and reducing alert fatigue, as well as where human expertise remains indispensable. We will discuss the balance between machine generated recommendations and human judgment, explore real world use cases, and highlight best practices for integrating AI into critical event management workflows. Join us to discover how organizations can leverage AI to improve situational awareness, accelerate response times, and make more informed decisions during high pressure incidents while keeping people at the center of the process. Key Topics: - What AI can and cannot do during critical events - AI assisted decision support - Reducing alert fatigue - Human judgment vs. machine recommendations If you're interested in exploring moderizing your incident response, you can try xMatters https://www.xmatters.com/

Russ d’Sa (CEO & Co-founder @ LiveKit) joins the show to deconstruct the "Product Paradigm Shift" toward voice-driven interfaces and agent-centric UX . We dive into LiveKit’s high-stakes scaling lessons: from powering OpenAI and Character AI’s voice mode, how they navigated real time bottlenecks to hit the next level of scale, the architectural necessity of a multi-cloud strategy, and the foundations of a co-founder relationships that can effectively blend engineering & business strategy.

As AI agents move past the “works on my machine” stage, the true challenge is equipping them with reliable, structured access to complex enterprise systems. Without it, AI agents are certain to be ineffective at enterprise scale. This session walks through how to evolve a data platform into a robust, agent-ready platform. We will focus on practical design patterns that make agents truly usable in real production environments. Key Takeaways: - Discover the architecture behind this transformation, including the use of a semantic layer, knowledge graph principles, and MCP-based tool abstractions. - Learn why structure, not just models, is the essential component for enabling reliable reasoning, robust governance, and seamless cross-system workflows. This webinar is designed for engineers and engineering leaders ready to move AI agent proofs-of-concept into secure, enterprise-scale production.

AI is changing how work gets done across engineering teams, but it’s also making performance harder to interpret. More activity, faster cycles, and new workflows don’t always translate into clear insights for leaders. In this session, we’ll break down how to build a more accurate view of engineering performance in the age of AI, focusing on how to connect day-to-day development work to delivery health, team effectiveness, and broader business goals. Key Discussion Topics: - AI increases activity but not necessarily clarity - Performance visibility must evolve with AI workflows - Engineering metrics should tie back to business outcomes For more data on how AI is changing engineering measurement, check out Harness’s State of Engineering Excellence report: https://www.harness.io/state-of-engineering-excellence To learn more about Harness AI DLC Insights check out this blog: https://www.harness.io/blog/introducing-ai-dlc-insights-to-prove-the-roi-of-your-ai-engineering-investment To see it in action: https://www.harness.io/demo/ai-dlc-insights

In this episode, Geddes Munson (SVP of Engineering @ Affirm) joins us to discuss operational / engineering excellence, scaling, and AI-native transformation! We explore Affirm’s approach to operational and engineering excellence and how a 2024 outage became a turning point in refining that focus. We deconstruct “AI retooling week”, the internal tools it inspired (including an incident tracing system), how the AI-native transition is impacting operational / engineering excellence, and how to connect these projects to business goals. Plus, we take a look at their early work building in agentic commerce, infrastructure decisions they made years ago setting them up for success now, how they’re thinking about designing for agent-first experiences.

Andrew McNamara, Director of Applied Machine Learning @ Shopify, joins the ELC podcast to share insights on building agentic platforms at scale, like Sidekick, that must keep reliability for its users at the forefront. Andrew describes the building philosophy behind Shopify and what it means to cultivate a culture of prototype-first while prioritizing hiring early-stage talent. We cover Sidekick’s development journey and how user feedback impacted its product vision, why evaluation is so important for determining ground truth sets, and the benefit of user-driven use cases. Andrew also dissects how they went about making product design decisions, such as building proactive agents and identifying subagent specializations.