InfoQ Homepage Artificial Intelligence Content on InfoQ
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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 2 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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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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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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Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review
Sarah Deitke shares how Duolingo scales AI adoption through engineering literacy programs and demonstrates how AI-driven code reviews reduced pull request merge times from 18 to 12 hours.
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Decision Models in Agentic Architectures: from Production to Agent Skills
Alex Porcelli explains how combining deterministic DMN decision models with agentic AI resolves enterprise AI challenges around accountability, rule governance, and non-deterministic logic.
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From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs
Cassie Shum shares lessons learned on using knowledge graphs as the foundation for agentic software systems, covering 4 key architectural patterns to optimize context, provenance, and team efficiency.
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Fixing the AI Infra Scale Problem by Stuffing 1M Sandboxes in a Single Server
Felipe Huici discusses scaling microVM sandboxes for AI workloads, explaining how millisecond cold boots, high density, and stateful scale-to-zero achieve efficient, secure cloud isolation.
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Platform Engineering in the Age of AI
The panelists discuss how internal developer platforms adapt for AI-assisted engineering, balancing standardization, guardrails, and developer autonomy to shape next-gen platform engineering.
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From AI Agent Demo to Production: Automated Testing and Evaluation
Zhou Yu explains why 95% of AI agents fail to reach production and shares how simulation-driven evaluation, synthetic users, and automated CI/CD testing unlock enterprise deployment.
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Beyond Prompting: Context Engineering for Production-Grade AI
Ricardo Ferreira explains how context engineering overcomes LLM latency, cost, and memory limitations in AI systems, sharing architectural lessons from building a custom Alexa backend with Redis.
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Running AI at the Edge: Running Real Workloads Directly in the Browser
James Hall explains why engineering teams should shift AI workloads on-device. He shares local inference strategies, WebGPU optimization techniques, and architecture choices for zero-trust privacy.
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