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.
Nitin Kanukolanu works on AI at Redis and specializes in building and deploying end to end machine learning systems. With experience developing production ML pipelines and fine tuning models, he focuses on applying modern machine learning and natural language technologies to solve real world customer problems.
