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[Video Podcast] Agentic Systems without Chaos: Early Operating Models for Autonomous Agents
In this episode, Shweta Vohra and Joseph Stein explore what changes when software systems start planning, acting, and making decisions on their own. The conversation distinguishes truly agentic use cases from traditional automation and looks at how architects and engineers should think about boundaries, orchestration, and system design in this new environment.
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2025 Key Trends: AI Workflows, Architectural Complexity, Sociotechnical Systems & Platform Products
In this end-of-year panel, the InfoQ podcast hosts reflect on AI’s impact on software delivery, the growing importance of sociotechnical systems, evolving cloud realities, and what 2026 may bring.
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Building a More Appealing CLI for Agentic LLMs Based on Learnings from the Textual Framework
Will McGugan, the maker of Textual and Rich frameworks, speaks about the reasoning of developing the two two libraries and the lesson learned. Also, he shares light on Toad, his current project, which he envisions being a more visually appealing way of interacting with agentic LLMs through command line.
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Platform Engineering for AI: Scaling Agents and MCP at LinkedIn
QCon AI New York Chair Wes Reisz talks with LinkedIn’s Karthik Ramgopal and Prince Valluri about enabling AI agents at enterprise scale. They discuss how platform teams orchestrate secure, multi-agentic systems, the role of MCP, the use of foreground and background agents, improving developer experience, and reducing toil.
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Elena Samuylova on Large Language Model (LLM)-Based Application Evaluation and LLM as a Judge
In this podcast, InfoQ spoke with Elena Samuylova from Evidently AI, on best practices in evaluating Large Language Model (LLM)-based applications. She also discussed the tools for evaluating, testing and monitoring applications powered by AI technologies.