How AI Agents Learn to Remember | Google's Context Engineering Deep Dive

Описание: What if your AI agent could actually remember you — not just what you said five minutes ago, but your preferences, your workflows, your past decisions?

In this deep dive, we break down Google's latest research paper on Context Engineering, the discipline that goes far beyond simple prompt engineering. We cover:

🔹 The 4-Phase Agent Lifecycle (Fetch → Prepare → Invoke → Upload)
🔹 Sessions as a Workbench — how agents manage ongoing conversations
🔹 Long-Context Compaction — what happens when context windows overflow
🔹 Memory vs RAG — why they're fundamentally different
🔹 Memory Manager Architecture — the central hub for all memory operations
🔹 Declarative vs Procedural Memory — knowing WHAT vs knowing HOW
🔹 Memory Organization Patterns — Collections, Structured Profiles, Rolling Summaries
🔹 Memory Scope & Multimodal Content — where memories live and what forms they take
🔹 Extraction, Consolidation & Provenance — how memories are created, updated, and trusted
🔹 Memory-as-a-Tool & Retrieval Pipeline — agent-driven memory access
🔹 Inference Injection — how memories are woven into prompts
🔹 Testing & Evaluation — 3 dimensions to measure memory quality
🔹 Production Architecture & Privacy — async event-driven design and security safeguards
🔹 The Complete Context Engineering Stack — Sessions, Memory, Production, Privacy

Whether you're building AI agents, designing LLM applications, or just want to understand the cutting edge of AI research, this video gives you the complete picture.

📄 Paper: "Context Engineering: Sessions & Memory" (Google, 2025)

⏱️ Timestamps:
00:00 Introduction — What is Context Engineering?
01:00 The 4-Phase Agent Lifecycle
02:00 Sessions as a Workbench
03:00 Long-Context Compaction Strategies
04:00 Memory vs RAG
05:00 Memory Manager Architecture
06:00 Declarative vs Procedural Memory
07:00 Memory Organization Patterns
08:00 Memory Scope & Multimodal Content
09:00 Memory Extraction Methods
10:00 Memory Consolidation & Relevance Decay
11:00 Provenance & Trust Hierarchy
12:00 Memory-as-a-Tool & Retrieval Pipeline
13:00 Inference with Memories
14:00 Testing & Evaluation
15:00 Production Architecture & Privacy
16:00 Summary & Key Takeaways

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