Introduction

What if the biggest limitation of artificial intelligence isn’t the model itself, but the information it receives, remembers, and uses to make decisions?

In this episode of The Deeep Dive, we explore the shift from prompt engineering to context engineering and examine why it is becoming a key part of building reliable, efficient AI agents for enterprise use.

Large language models can process enormous amounts of information, but larger context windows do not automatically produce better answers. Irrelevant documents, outdated data, conflicting instructions, and inefficient memory management can increase costs, slow responses, and lead to hallucinations.

The solution involves more than writing better prompts. It requires a system that selects relevant information, manages short-term and long-term memory, connects AI models to external tools, and ensures that every response is grounded in the right data.

What you’ll learn in this episode

1. From prompt engineering to context engineering

Understand the difference between writing instructions for an LLM and designing the information environment in which it operates. Explore context poisoning, context distraction, context confusion, and context clash, and learn why information quality matters as much as the prompt itself.

2. Why LLM memory creates a hardware bottleneck

Discover how the key-value (KV) cache affects AI inference, GPU memory usage, response latency, and concurrent users. We examine techniques such as PagedAttention, quantization, token eviction, and neural compaction that aim to make long-context processing more efficient.

3. How AI agents build persistent memory

Explore the four layers of AI agent memory: short-term, episodic, semantic, and procedural memory. Learn how frameworks such as Letta and Mem0 approach memory management, retrieval, summarization, and the challenge of retaining useful information across multiple sessions.

4. How MCP connects AI agents to business systems

Learn how the Model Context Protocol (MCP) provides a standardized way for AI applications to discover and use external tools and data sources. The discussion covers integrations with databases, CRMs, email, developer tools, and other enterprise systems, along with permissions and human approval for sensitive actions.

5. Why enterprise AI needs context governance

Understand the principle of minimal sufficiency: giving an AI the smallest relevant set of reliable information needed to complete a task. We explore structured metadata, data lineage, access controls, source verification, and knowledge graphs as ways to improve accuracy and reduce conflicting information.

6. RAG vs. GraphRAG: Connecting information across sources

Traditional retrieval-augmented generation finds relevant passages, while GraphRAG can use explicit relationships between entities to connect facts across documents and systems. Learn how these approaches help AI agents answer questions that require multiple steps of reasoning.

7. The future of autonomous and multi-agent AI

Explore just-in-time retrieval, progressive context loading, agent-to-agent coordination, and persistent scratchpads. The episode closes with the idea of sleep-time compute, where AI systems could process past experiences during idle periods to improve future performance.

Who should listen?

This episode is for AI engineers, software developers, enterprise architects, CTOs, CIOs, data and analytics leaders, technology consultants, and business leaders evaluating agentic AI.

It is also useful for anyone exploring how LLMs work, why AI agents sometimes lose context, and what businesses need to build AI systems that can operate reliably across complex workflows.

Listen to the episode

Join The Deeep Dive for a practical discussion on the engineering behind modern AI agents, from GPU memory optimization and persistent memory to MCP integrations, knowledge graphs, and autonomous systems.

The central question is simple: How do we build AI that knows what to remember, what to retrieve, and when to act?