AI Architecture12 min read
Architecting AI Copilots: Multi-Agent Orchestration & Contextual RAG
By Raghav Shah•
A simple chat interface is not enough. Premium AI platforms utilize specialized agents that communicate and execute tasks in parallel.
Context loss and hallucination in LLM prompts
Feeding too much data into a single prompt confuses the AI model, causing hallucinations. Furthermore, long conversations run into token limit boundaries.
The Solution: RAGS-AI-APP Tech Architecture
By designing a Retrieval-Augmented Generation (RAG) system, we search database documents, retrieve the most relevant snippets, and route them to specific sub-agents.
Multi-Agent Routing Logic
// Node.js agent router based on task classifications
const routeTask = async (taskDescription) => {
const response = await openai.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'system', content: 'Classify this task: CODER, RESEARCHER, or COPYWRITER.' }, { role: 'user', content: taskDescription }]
});
const agentType = response.choices[0].message.content.trim();
return executeAgent(agentType, taskDescription);
};Key Insights & Takeaways
- ✓ Vector databases (like Pinecone) query context snippets in milliseconds
- ✓ Dynamic system prompts ensure sub-agents stay focused on their tasks
- ✓ Storing conversation memories in Redis preserves chat history states
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