Code To Culture, Ep 5: Platform Engineering Futures: The Role of Gen AI and Observability скачать в хорошем качестве

Code To Culture, Ep 5: Platform Engineering Futures: The Role of Gen AI and Observability

Welcome to CodeToCulture — Platform Engineering Unpacked for Enterprise, a panel series on ‪@cloudnativefm‬ @CloudTherapist where the brightest minds in platform engineering reveal battle-tested best practices: defining the discipline, measuring meaningful impact, and applying emerging technologies, especially Gen-AI and modern observability, to transform how engineering teams operate. Previous episodes in this series Ep. 1 Internal Developer Platforms: Hype or the Real Future of DevOps?    • Code To Culture, Ep 1: Internal Developer ...   Ep. 2 From DevOps to Platform Engineering    • SECRETS Behind Building Strong Platform Te...   Ep. 3 Golden Paths vs Developer Freedom: Can We Both?    • Code To Culture, Ep 3: Golden Paths vs Dev...   Ep. 4 SECRETS Behind Building Strong Platform Teams    • You MUST Watch THIS If You Want Your Platf...   …and now, Ep. 5, where we look ahead. Episode 5 — About this conversation In this episode, Saim Safdar and Cortney Nickerson host Ravi Lachman (Harness), Mihir Vora (Capital One + CDF ambassador), to discuss the next chapter for platform teams: how to treat platforms as products, how to meaningfully measure success, and where Gen-AI + observability collide — from knowledge discovery to automated remediation (and the guardrails we must design). Timecodes 0:00 Series intros 1:27 Guest intros 3:45 Biggest inflection points in platform engineering adoption 5:23 How platform teams are structured today 7:46 Measuring success: ROI, DORA, activation, developer experience 11:18 Cultural challenges: moving from DevOps → Platform Engineering 14:04 KPIs vs long-term platform transformation — tradeoffs & pitfalls 22:00 Where Gen-AI is being applied inside platforms (patterns & risk appetite) 23:05 Gen-AI for knowledge discovery and agentic workflows (semantic layer) 25:25 Observability that takes action — automation vs human judgment 26:35 Guardrails, accountability & risk tolerance for agentic actions 29:00 Early surprises with Gen-AI adoption 31:00 Gen-AI: Cautious adopters vs early experimenters 35:00 Governance, compliance, and data protection concerns in regulated orgs 39:00 Rapid prototyping with AI 43:12 Migration acceleration: how agents help with large refactors and modernizations 46:00 Judgment can’t be fully automated — the limits of agentic automation 49:12 Where platforms make thousands of micro-decisions 53:11 Consumerization of enterprise: 58:14 Vision: platforms lifting heavy lifting for developers; closing thoughts & wrap