"The full program for QCon.ai New York 2025, taking place December 16-17, 2025, is now available. Designed for senior software engineers, architects, and technology leaders, this year's conference focuses on one of the toughest challenges in modern software: turning AI prototypes into reliable, production-grade systems that scale. Curated by a Program Committee of senior practitioners, QCon AI New York delivers in-depth, experience-driven sessions that address the realities of building, deploying, and maintaining AI systems in the enterprise."
"Reisz highlighted several sessions from the program that best represent the conference's engineering-first approach and its value for senior developers, architects, and technical leaders navigating the next stage of enterprise AI adoption: AI's Impact on the Software Development Lifecycle The integration of AI is creating fundamental shifts in the software development lifecycle (SDLC). These sessions address the new challenges and opportunities for engineering teams. "AI Works, Pull Requests Don't: How AI Is Breaking the SDLC and What To Do About It": Michael Webster, a Principal Engineer at CircleCI with deep experience in developer tooling, will discuss the growing friction between AI-generated code and established quality gates like pull requests and CI pipelines. The talk will explore new processes required to maintain velocity without sacrificing code quality. "Platform Teams Enabling AI - MCP/Multi-Agentic Tools Across LinkedIn": this session features LinkedIn Principal Engineers Karthik Ramgopal and Prince Valluri. They will cover the complex challenge of building centralized platform infrastructure to support a wide array of AI-dri"
The full program for QCon.ai New York 2025 is scheduled for December 16-17, 2025 and targets senior software engineers, architects, and technology leaders. The conference centers on converting AI prototypes into reliable, production-grade systems that scale. A Program Committee of senior practitioners curated the program to provide in-depth, experience-driven sessions addressing the realities of building, deploying, and maintaining enterprise AI systems. The agenda emphasizes engineering-first approaches and practical lessons on how AI alters the software development lifecycle, creates friction with existing quality gates like pull requests and CI pipelines, and demands centralized platform infrastructure to support diverse AI-driven applications.
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