InfoQ Homepage Artificial Intelligence Content on InfoQ
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Designing AI Platforms for Reliability: Tools for Certainty, Agents for Discovery
Aaron Erickson shares how NVIDIA scales AI agent hierarchies by balancing determinism and discovery, offering engineering leaders a practical blueprint for building reliable, purpose-built systems.
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Fine Tuning the Enterprise: Reinforcement Learning in Practice
Will Hang and Wenjie Zi explain how OpenAI's Agent RFT (Reinforcement Fine-Tuning) optimizes reasoning models end-to-end, enabling autonomous agents to learn efficient tool use and cut latencies.
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Graph RAG: Building Smarter Retrieval Workflows with Knowledge Graphs
Cassie Shum explains how to move beyond traditional RAG limits using GraphRAG to inject enterprise logic and complex multi-hop reasoning into scalable knowledge graphs within a data warehouse.
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Trustworthy Productivity: Securing AI-Accelerated Development
Sriram Madapusi Vasudevan explains how to secure autonomous AI agents, sharing enterprise patterns like provenance gates and sandboxed runtimes to defend the ReAct loop against rogue execution.
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AI Works, Pull Requests Don’t: How AI is Breaking the SDLC and What to Do about it
Michael Webster explains how autonomous "headless" AI agents are flooding pipelines with code. He shares how CircleCI addresses the resulting technical debt and bottlenecked PR reviews.
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Rules for Understanding Language Models
Naomi Saphra explains why language models act like populations rather than individuals. She discusses how data diversity drives generalization, why LLMs mirror users and how tokenizers alter behavior.
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AI Agents to Make Sense of Data at OpenAI
OpenAI’s Bonnie Xu explains Kepler, their internal AI data analyst agent built on MCP. She shares how they scale data discovery across 600+ PB using automated context, RAG, and AST-based evals.
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From Hype to Strong Foundations: What the Rise, Fall and Resurgence of Agents Can Teach Us about Outlasting the Cycle
Aditya Kumarakrishnan discusses "Agents: The Missing Manual," sharing four historically grounded ideas to build modular, durable, and hyper-tenant AI agent architectures.
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Automating the Web with MCP: Infra that Doesn’t Break
Paul Klein explains how to automate the web with MCP. He shares architectural strategies for running multi-tenant, cloud-hosted Chromium sandboxes to power AI browsing agents.
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Moving Mountains: Migrating Legacy Code in Weeks Instead of Years
Principal AI Engineer David Stein explains how ServiceTitan uses AI coding agents to automate large-scale legacy code migrations, compressing quarters of technical debt into weeks.
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Beyond Prompting: Context Engineering and Memory Management for AI Systems at Scale
Adi Polak explains how to scale agentic AI by shifting from stateless prompt engineering to stateful, low-latency context engineering with Apache Kafka and Flink.
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Platform Teams Enabling AI - MCP/Multi-Agentic Tools across Linkedin
LinkedIn’s Karthik Ramgopal and Prince Valluri explain how to scale engineering with agentic AI. They discuss building centralized platform foundations for orchestration, tooling, and context.
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