Scaling the Agentic Enterprise: From Vibe-Coded Chatbots to Reliable Production Systems You Can Trust

Senior Applied Scientist with 10+ years of expertise in Machine Learning, Generative AI Research, and Distributed Systems. Specialized in solving hard, undefined problems in Causal Inference and Deep Learning, with a proven ability to scale theoretical research into production-grade solutions. Expert in large-scale Generative AI applications and Cloud Services, bridging the gap between novel algorithm design and robust system architecture. Holder of two patents in ML aided serverless auto-scaling, demonstrating deep technical leadership in building high-performance systems. Reference: The LLM revolution has compressed conversational AI prototyping from months to days. "Vibe coding" delivers impressive demos—but these rapid prototypes fail to keep up at enterprise scale. Architectural anti-patterns like stuffing massive, unstructured payloads into monolithic context windows—done for quick prototyping—lead to context rot, instruction-following degradation, and systemic fragility under production load and diversity. This talk presents a battle-tested roadmap for evolving from prototype to production-grade agentic chatbot systems, drawn from deploying and maintaining high-visibility LLM applications at scale. I’ll share the scientific and engineering principles that bridge the “demo-to-deployment” gap across three critical dimensions: 1. Scientific Rigor in Agentic Architecture Move beyond monolithic context-stuffing to modular Agentic RAG frameworks. Learn how to isolate bounded agent scopes, decouple capabilities, and optimize signal-to-noise ratios to preserve reasoning quality without inducing context overload. Discover architectural patterns including steering files for deterministic behavior control, event-driven hooks for dynamic capability composition, and structured prompt decomposition—transitioning from “dump everything into the prompt” to architectures where each agentic step operates on precisely the information it needs. 2. Continuous Empirical Evaluation Replace subjective “vibe checks” with comprehensive automated evaluation frameworks. Explore LLM-as-judge pipelines for semantic quality assessment, RAGAS based metrics for retrieval precision and context relevance, and agent-core evaluation standards for continuous monitoring in production. Learn how to statistically bound variance, detect hallucination creep, and identify silent degradation—enabling validation at scale without human bottlenecks. 3. The Horizontal Scaling and Reliability Frontier Understand state-of-the-art orchestration patterns that enable horizontal scaling while maintaining constant reliability. Explore semantic contracts for agent capability boundaries, adaptive routing with confidence-based fallbacks, distributed checkpoint management, and knowledge engine construction for unified information retrieval across distributed sources. What You'll Take Away: A proven, structured methodology for building deterministic, observable, and scalable agentic chatbots capable of safely serving millions of users.