#2 Routing Design Pattern Explained | Smarter Agentic AI Systems скачать в хорошем качестве

#2 Routing Design Pattern Explained | Smarter Agentic AI Systems

🔀 Routing Design Pattern | Agentic AI Explained In this video, we break down the Routing Design Pattern, a core concept in agentic AI systems that enables large language models (LLMs) to dynamically choose the right path, tool, or agent based on user intent and context. Instead of hard-coded flows, routing allows AI systems to become adaptive, scalable, and efficient, making it a critical pattern for real-world production systems. 🔍 What You’ll Learn What the Routing Design Pattern is and why it matters How routing works inside agentic and LLM-based systems Rule-based vs model-based routing Common routing strategies: Intent-based routing Tool routing Agent selection Workflow branching How routing fits with Prompt Chaining, Tool Use, and RAG 🧠 When to Use Routing Use this pattern when: Multiple tools or agents are available User queries vary in intent or complexity You want to reduce cost, latency, and token usage Systems must scale beyond linear pipelines ⚠️ Challenges & Pitfalls Covered Misrouting and fallback strategies Confidence thresholds and uncertainty handling Observability and debugging routing decisions Guardrails and safety constraints 👨‍💻 Who This Video Is For LLM & Agentic AI Engineers Backend & Platform Engineers AI Architects & Researchers Anyone building multi-path AI workflows 👍 Support the Channel If you found this helpful: Like the video Subscribe for more Agentic AI & LLM system design Comment with your use case or questions Routing Design Pattern, Agentic AI, LLM Routing, AI Architecture, Tool Routing, Multi Agent Systems, Generative AI #AgenticAI #AgenticDesignPatterns #LLMAgents #AIArchitecture #RAG #MultiAgentSystems #AutonomousAI #Routingdesignpattern #AISystemDesign #GenerativeAI