Context Engineering for LLM Agents (Production-Ready Agents #3) скачать в хорошем качестве

Context Engineering for LLM Agents (Production-Ready Agents #3)

Context windows fill up fast in long-running agent tasks. When agents hit their token limits, they lose critical information, make poor decisions, and fail unexpectedly. In this video, I show you four production grade context engineering strategies that let your agents handle 10+ turn conversations, 50+ tool calls, and massive documents without breaking. I'll walk through: • The problem: context overflow, poisoning, distraction, confusion, and clash • Four core strategies: WRITE, SELECT, COMPRESS, ISOLATE • Five real production scenarios with step-by-step implementations • Self-healing patterns and error recovery techniques • When to use each strategy and how to combine them Whether you're building customer support agents, research assistants, or enterprise automation systems, you'll learn how to keep your agents fast, reliable, and cost-effective. 📚 Other videos in this series: • Semantic Caching Video:    • Make LLM Agents Faster and Cheaper with Se...   • Dynamic Tool Call:    • Managing Many Tools for LLM Agents: What A...   ⏱️ TIMESTAMPS: (00:00) Intro: The Context Problem (01:31) Four Context Failures: (Context Poisoning, Context Distraction, Context Confusion, Context Clash) (2:29) Lost in the Middle (4:00) Strategy #1: WRITE (External Memory) (5:18) Strategy #2: SELECT (Smart Retrieval) (6:32) Strategy #3: COMPRESS (Summarization) (7:49) Strategy #4: ISOLATE (Sub-Agents) (10:05) Scenario 1: 10-Turn Conversations (10:25) Scenario 2: 50+ Tool Calls Per Task (10:48) Scenario 3: Multiple Domain Expertise (11:12) Scenario 4: Large Document Processing (11:29) Scenario 5: Self-Healing After Failures (11:53) Best Practices & Key Takeaways 🔗 REFERENCES: • DeepMind Research: https://storage.googleapis.com/deepmi... • Databrick Research: https://www.databricks.com/blog/long-... • Lost in the Middle: https://arxiv.org/pdf/2307.03172 • Manus AI (Context Engineering for AI Agents): https://manus.im/blog/Context-Enginee... • Anthropic (How we built our multi-agent research system): https://www.anthropic.com/engineering... 🌐 CONNECT WITH ME: LinkedIn:   / farzad-roozitalab   X: https://x.com/Farzad_Rzt GitHub: https://github.com/Farzad-R 📌 KEY CONCEPTS COVERED: #ContextEngineering #LLMAgents #ProductionAI #AgentOptimization #LLMEngineering #AIArchitecture #SemanticRetrieval #VectorSearch #AgentMemory #SelfHealingAgents #EnterpriseAI #aiagents 🔔 Don't forget to LIKE and SUBSCRIBE if this helps you build production-ready AI systems!