Why do AI agents struggle with long conversations, outdated information, and complex enterprise tasks? The answer goes beyond prompt engineering.
In this episode of The Deeep Dive, we explore the shift from prompt engineering to context engineering and examine how AI systems manage information, memory, and real-world actions.
Discover how LLM context windows and KV caches affect inference speed and GPU memory, why adding more data can reduce AI accuracy, and how techniques such as PagedAttention, quantization, token eviction, and neural compaction address memory constraints.
We also unpack the four layers of AI agent memory, Retrieval-Augmented Generation (RAG), GraphRAG, the Model Context Protocol (MCP), enterprise context governance, and multi-agent coordination.
Finally, we explore sleep-time compute and the possibility of AI agents learning from past experiences to improve future performance.
Whether you're building AI agents, leading enterprise AI adoption, or exploring the next phase of artificial intelligence, this episode explains the architecture behind more efficient, context-aware AI systems.
Topics covered:
• Prompt engineering vs. context engineering
• LLM memory, KV cache, and inference optimization
• AI agent memory architecture
• RAG vs. GraphRAG
• Model Context Protocol (MCP) and AI tool integrations
• Enterprise AI governance and data security
• Autonomous agents and multi-agent systems
• Sleep-time compute and continuous learning
#ContextEngineering #AIAgents #ArtificialIntelligence #AgenticAI #LLM #MCP #GraphRAG #RAG #EnterpriseAI #GenerativeAI
