#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

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Routing Design Pattern, Agentic AI, LLM Routing, AI Architecture, Tool Routing, Multi Agent Systems, Generative AI

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