InfoQ Homepage Artificial Intelligence Content on InfoQ
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Making AI Agents Work for You (and Your Team)
Hannah Foxwell explains how to design agent teams for high-quality output and shares a vision where AI agents handle toil, freeing humans to focus on creativity and customer relationships.
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10 Reasons Your Multi-Agent Workflows Fail and What You Can Do about It
Victor Dibia discusses multi-agent systems, detailing how to build them with AutoGen, common failure points, and strategic approaches for senior software developers and engineering leaders.
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From Autocomplete to Agents: AI Coding State of Play
Birgitta Böckeler explains how to use AI coding assistants effectively and responsibly, from fighting complacency to fostering a healthy team culture.
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Key Lessons from Shipping AI Products beyond the Hype
Phil Calçado shares key learnings from building and scaling an AI startup, offering a product-centric approach for engineering leaders and architects navigating generative AI.
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The Form of AI
Savannah Kunovsky, who leads IDEO's Emerging Tech Lab, explains how combining engineering rigor with design thinking creates impactful, user-centered AI products.
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Enhance LLMs’ Explainability and Trustworthiness with Knowledge Graphs
Leann Chen discusses how knowledge graphs provide structured data to enhance LLM accuracy, tackling common challenges like hallucinations and the "lost-in-the-middle" phenomenon in RAG systems.
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AI Agents & LLMs: Scaling the Next Wave of Automation
The panelists discuss AI agents and LLMs, exploring their definitions, architectures, use cases, reliability, and impact on the SDLC and future of automation.
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A Framework for Building Micro Metrics for LLM System Evaluation
Denys Linkov discusses critical lessons for senior developers and leaders on building robust LLM systems and actionable metrics that prevent production issues and drive business value.
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What to Pack for Your GenAI Adventure
Soledad Alborno discusses essential skills and new tools for building successful Generative AI products.
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Scaling Large Language Model Serving Infrastructure at Meta
Ye (Charlotte) Qi explains key considerations for optimizing LLM inference, including hardware, latency, and production scaling strategies.
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How Green is Green: LLMs to Understand Climate Disclosure at Scale
Leo Browning explains the journey of developing a Retrieval Augmented Generation (RAG) system at a climate-focused startup.
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GenAI for Productivity
Mandy Gu shares Wealthsimple's journey leveraging generative AI for productivity and operational optimization.
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