InfoQ - AI, ML & Data Engineering - Presentations

Presentation: What 25 Trillion Tokens Reveal About How Developers Actually Adopt AI Agents

Brendan O’Leary — Thu, 15 Oct 2026 11:00:00 GMT

Brendan O'Leary discusses how real-world AI adoption depends on building trust rather than chasing model benchmarks. Drawing from 40 trillion tokens of usage data, he explains the four rungs of the AI adoption ladder and analyzes key failure points in context construction, multi-model routing, and feedback loops to help engineering leaders successfully scale agentic workflows.

By Brendan O’Leary

Presentation: You Can't Trust Your AI Bill. And Neither Can I

Erik Peterson — Mon, 12 Oct 2026 11:00:00 GMT

Erik Peterson discusses why AI spend is eclipsing cloud bills, triggering a major software engineering cost crisis. He explains tokenmaxxing, unit economics, forced developer promotions, and why fully autonomous AI will write, deploy, and govern code by 2028.

By Erik Peterson

Presentation: Ontology‐Driven Observability: Building the E2E Knowledge Graph at Netflix Scale

Prasanna Vijayanathan, Renzo Sanchez-Silva — Fri, 09 Oct 2026 11:00:00 GMT

Prasanna Vijayanathan and Renzo Sanchez-Silva share how Netflix tackles observability across 38M events/sec. They discuss replacing reactive monitoring with an AI-driven operational ontology and agentic workflows using Claude and graph databases. They explain how unifying MELT telemetry into queryable knowledge graphs enables automated triaging, root-cause analysis, and self-healing systems.

By Prasanna Vijayanathan, Renzo Sanchez-Silva

Presentation: Multi-Agent Patterns from Spotify’s AI Powered Advertising Platform

Pratik Rasam — Thu, 08 Oct 2026 11:00:00 GMT

Pratik Rasam discusses how Spotify Ads Manager runs production-grade multi-agent systems at scale using Google ADK Java. He shares key architectural patterns, domain ownership models, deterministic guardrails, and tracing-based evaluation strategies, detailing hard-learned lessons on drawing agent boundaries, optimizing tool schemas, and managing costs while avoiding monolithic agent pitfalls.

By Pratik Rasam

Presentation: Building Reusable Evaluation Frameworks for Agentic AI Products

Susan Chang — Mon, 05 Oct 2026 11:19:00 GMT

Susan Chang explains how Elastic transitioned from siloed, ad-hoc AI agent evaluations to a unified, production-grade framework. She discusses balancing LLM-as-a-judge with deterministic rules, bridging Python data science evals with TypeScript production code, and implementing deep tracing to catch regressions across complex RAG and cybersecurity workloads while preserving domain context.

By Susan Chang