feat(docs): autonomous structured expansion on ai-agent-semantic-routing

README.md

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       * 📄 [readme.md](./backend/nodejs/readme.md)
   * 📄 **[docs/](./docs/)**
     * 🤖 [ai-agent-orchestration.md](./docs/ai-agent-orchestration.md)
+    * 🤖 [ai-agent-semantic-routing.md](./docs/ai-agent-semantic-routing.md)
     * 🤖 [ai-agent-self-healing-architectures.md](./docs/ai-agent-self-healing-architectures.md)
     * 🤖 [vibe-coding-agents.md](./docs/vibe-coding-agents.md)
     * 🤖 [vibe-coding-deterministic-patterns.md](./docs/vibe-coding-deterministic-patterns.md)
@@ -397,6 +398,7 @@ graph TD
       * 📄 [readme.md](./backend/nodejs/readme.md)
   * 📄 **[docs/](./docs/)**
     * 🤖 [ai-agent-orchestration.md](./docs/ai-agent-orchestration.md)
+    * 🤖 [ai-agent-semantic-routing.md](./docs/ai-agent-semantic-routing.md)
     * 🤖 [ai-agent-self-healing-architectures.md](./docs/ai-agent-self-healing-architectures.md)
     * 🤖 [vibe-coding-agents.md](./docs/vibe-coding-agents.md)
     * 🤖 [vibe-coding-deterministic-patterns.md](./docs/vibe-coding-deterministic-patterns.md)

docs/ai-agent-semantic-routing.md

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+---
+technology: Semantic Routing
+domain: Documentation
+level: Senior/Architect
+version: Latest
+tags: [ai agents, semantic routing, orchestration, vibe coding, 2026 trends]
+ai_role: Architectural Strategist
+last_updated: 2026-04-02
+---
+
+# 🤖 Semantic Routing High-Value Optimization
+
+When configuring Semantic Routing for multi-agent systems, adhering to strict best practices ensures scalable and deterministic execution in AI agent orchestration.
+
+> [!NOTE]
+> Semantic Routing is critical for directing LLM queries to the appropriate agent or tool based on embedding proximity, rather than rigid keyword matching.
+
+## 🏗️ Architectural Topology
+
+A robust Semantic Routing architecture isolates intent detection from task execution.
+
+```mermaid
+graph TD
+    UserQuery[User Query] --> Embeddings[Vector Embeddings]
+    Embeddings --> Router[Semantic Router]
+    Router -->|Intent: Code| CodeAgent[Code Agent]
+    Router -->|Intent: Docs| DocsAgent[Documentation Agent]
+    Router -->|Intent: Unknown| Fallback[Fallback Protocol]
+
+    class UserQuery auto_style_UserQuery
+    class Embeddings auto_style_Embeddings
+    class Router auto_style_Router
+
+    classDef default fill:#e1f5fe,stroke:#03a9f4,stroke-width:2px,color:#000;
+    classDef auto_style_UserQuery fill:#e3f2fd,stroke:#1565c0,stroke-width:2px;
+    classDef auto_style_Embeddings fill:#ff9800,stroke:#f57c00,stroke-width:2px,color:#fff;
+    classDef auto_style_Router fill:#4caf50,stroke:#388e3c,stroke-width:2px,color:#fff;
+```
+
+---
+
+## ⚖️ Configuration Constraints
+
+The following constraints define the optimal state for routing systems.
+
+| Component | Constraint | Fallback Strategy |
+| :--- | :--- | :--- |
+| **Embeddings Model** | Must be statically typed or version-locked | Local lightweight model |
+| **Similarity Threshold** | `> 0.85` strictly enforced | Reroute to clarification loop |
+| **Latency Limit** | `< 150ms` p95 | Cache frequent queries in Redis |
+
+---
+
+## 🔄 Intent Mapping Pattern
+
+### ❌ Bad Practice
+
+```typescript
+function routeQuery(query: string) {
+    // Rigid if-else logic fails with varied natural language
+    if (query.includes('fix bug')) {
+        return codeAgent(query);
+    } else {
+        return generalAgent(query);
+    }
+}
+```
+
+### ⚠️ Problem
+
+Hardcoded keyword matching ignores semantic intent. It results in brittle routing logic that fails to scale with the complexity of user prompts, leading to high error rates and agent hallucinations when context is misaligned.
+
+### ✅ Best Practice
+
+```typescript
+import { SemanticRouter } from 'semantic-router';
+
+async function processQuery(query: unknown) {
+    if (typeof query !== 'string') {
+        throw new Error('Invalid query type');
+    }
+
+    // Leverage vector embeddings for intent classification
+    const route = await SemanticRouter.classify(query, { threshold: 0.85 });
+
+    if (route === 'CODE_FIX') {
+        return codeAgent(query);
+    }
+
+    return generalAgent(query);
+}
+```
+
+### 🚀 Solution
+
+By utilizing vector embeddings and a dynamic similarity threshold, the system autonomously maps varied phrasing to the correct underlying intent. This guarantees deterministic routing paths and scales effortlessly across multi-agent environments.
+
+---
+
+> [!IMPORTANT]
+> Always decouple the semantic router from the execution agents to allow independent scaling.
+
+## ✅ Actionable Checklist
+
+- [ ] Lock the embedding model version to prevent vector drift.
+- [ ] Define explicit thresholds for semantic similarity.
+- [ ] Implement a fallback agent for low-confidence routing scenarios.
+- [ ] Cache highly frequent semantic matches using Redis.
+
+[Back to Top](#)