InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Ontology‐Driven Observability: Building the E2E Knowledge Graph at Netflix Scale
Prasanna Vijayanathan and Renzo Sanchez-Silva explain how Netflix uses AIOps, AI agents, and operational ontologies to turn high-scale telemetry into automated, proactive incident root-cause analysis.
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Multi-Agent Patterns from Spotify’s AI Powered Advertising Platform
Pratik Rasam explains how Spotify Ads Manager builds multi-agent AI systems in production using Google ADK Java, detailing guardrails, domain ownership, and key architectural patterns for scale.
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Building Reusable Evaluation Frameworks for Agentic AI Products
Susan Chang shares how Elastic built a production-grade AI agent evaluation framework, detailing lessons on tracing, LLM-as-a-judge, programmatic evals to prevent regression across engineering teams.
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Building GenAI Platform at DoorDash
Swaroop Chitlur and Sidd Kodwani explain how DoorDash built and scaled an enterprise GenAI platform, revealing key architectural bets, pivots, and operational principles driving real business impact.
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Beyond Observability: Evolving Production Operations in the Age of AI
The panelists share how AI and automation reshape production operations, turn system data into actionable insights, and change architectural practices for modern, complex software delivery.
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Context is the New Code
Patrick Debois explains how treating context like code transforms the Software Development Life Cycle into a Context Development Life Cycle to scale AI agent workflows reliably across teams.
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From Consumers to Builders: Turning 200 of our Team into Agent Creators in Two Weeks
Ben Maraney explains how Forter enabled R&D teams to build internal AI agents fast by simplifying tools, using custom MCP servers, and removing organizational roadblocks to maximize engineering ROI.
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Adaptive Recommenders in the Real World: Inference, Evals, and System Design
Mallika Rao shares why building adaptive recommendation systems requires shifting focus from isolated ML models to real-time feedback loops, retrieval freshness, and production-level constraints.
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Designing Fast, Delightful UX with LLMs for Mobile Frontends
Balakrishnan Ramdoss explains how to scale AI-driven conversational app experiences using server-driven UI, BFF architectures, optimized prompting & high-performance on-device models for low latency.
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APIs for Agents: Rethinking API Programs in the MCP Era
Jim Gough and Andreea Niculcea explain how Morgan Stanley leverages Architecture as Code with CALM and MCP to scale secure, compliant API platforms for AI agents and future protocols like A2A.
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The Agent Harness: Control Planes, Invariants, and Approval Boundaries for Production AI Agents
OpenAI’s Vinoth Govindarajan explains how to build reliable AI agent harnesses in production, focusing on state ownership, mutation ordering, scoped authority, and user-visible proof of action.
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Context Engineering at LinkedIn: How We Built an Organizational Context Layer for AI Agents with MCP
Ajay Prakash shares how LinkedIn built Contextual Agent Playbooks and Tools to give AI coding agents internal stack context, automating 600+ workflows and raising productivity 20%.
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