As LLMs and agents become dramatically smarter, the real challenge shifts from model capability to context engineering. Powerful agents can reason, plan, and use tools — but without the right context, they hallucinate, waste tokens, and degrade in performance.
This talk explores why context engineering is essential for building reliable, production-ready AI systems. We’ll move beyond traditional RAG to agentic workflows, examine common context failure modes, and discuss how to unify memory, structured data, APIs, and retrieval into a cohesive context strategy.
Vishal Bala is a Senior Applied AI Engineer at Redis based in Berlin, with experience spanning applied AI, machine learning, data engineering, and analytics. He has held senior data science and leadership roles at companies including Scoutbee, resourcly, and Automation Hero, where he focused on LLMs, MLOps, generative AI products, and scalable production systems. Originally trained as an astrophysicist, he earned honors in Astronomy & Physics from Boston University and has published peer-reviewed research before transitioning into industry AI.
