Gen AI Observability for Engineering Leaders

Gen AI Observability for Engineering Leaders Interactive Workshop + Live Demo | 90 Minutes 🔍 Your Team Adopted AI Tools — Now What? Every engineering org is deploying AI coding assistants, agentic workflows, and foundation models. But here's the uncomfortable truth: most have zero visibility into whether it's actually working. How much are you spending per commit on AI tokens? Are your developers faster — or just busier? Which of your 30+ AI tools are delivering ROI and which are waste? Join us for a 90-minute hands-on workshop where we'll show you a battle-tested framework for measuring AI's real impact on engineering velocity, code quality, and cost — built entirely on AWS-native services with zero per-seat licensing. What You'll Learn 📊 Measure Real Impact — Move beyond vanity metrics. Track adoption, productivity, and outcomes across your entire engineering org with a proven KPI framework 🔥 AI Coding Tool Economics — Get full visibility into 40+ AI coding tools (Claude Code, Cursor, Codex, Gemini, Grok & more). See token usage, model attribution, cost-per-commit, and waste detection in one place 🏗️ AWS-Native Architecture — Deploy dashboards on CloudWatch, QuickSight, Bedrock, and DynamoDB. No vendor lock-in, no per-seat fees — just your data, your infrastructure 🛡️ Security & Governance — Address the 8 CISO concerns keeping your security team up at night. Shadow AI detection, compliance guardrails, and policy enforcement built in 📈 Maturity Assessment — Understand where your org sits on the AI transformation curve and get a concrete roadmap to level up Live Demo: What You'll See in Action ✅ Executive Readout Dashboard — CTO-level view of AI impact across teams ✅ Token Economics Console — CFO-friendly cost attribution ($/commit, tokens/line) ✅ Team Velocity Tracker — Engineering manager view of before vs. after ✅ CISO Compliance Dashboard — Governance, data flow, and policy status ✅ AI-DORA Analysis — AI-native twist on DORA metrics for modern…

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