Forget chatbots—enterprise AI agents are becoming the new operating layer for work. We’re entering a phase where AI agents don’t just assist, they collaborate, reason, and execute across enterprise systems. But which frameworks—AutoGen, LangChain, custom orchestrators—are truly battle-tested in the wild? And how do you get from brittle prototypes to structured, multi-agent systems with real value flow? Infrastructure, orchestration, agent frameworks, inference, and securing the AI stack.

If your GenAI project doesn’t pay off by Q4, your CFO is pulling the plug. 

Enterprises are sitting on thousands of AI pilots—but only a few are turning into scaled systems that drive real revenue, cost reduction, or efficiency gains. We deliver the cold, hard math behind successful deployments: which use cases are paying off, how to measure impact in 

Everyone’s fine-tuning models, but no one’s feeding them right. 

Garbage in, hallucination out. Your AI is only as good as your data pipeline—and most companies are still duct-taping unstructured inputs into half-baked models. Explores how technical leaders are re-architecting data foundations: from governance and quality to retrieval-augmented generation (RAG), synthetic data, and real-time observability.

You don’t need the biggest model—you need the smartest stack. 

As inference costs spike and GPU supply chains tighten, the enterprises that win are those who master model strategy and compute tradeoffs. From running Groq's blazing-fast inference chips to rethinking open-weight model tuning, this track lays out the path to affordable, performant AI—without breaking your budget or your team.