Multi-Agent AI Platform
Serving 30+ custom AI assistants from a single runtime
A consultancy needed to offer personalized AI assistants to each of their clients, but deploying and maintaining separate instances for 30+ configurations was impractical. Each client required unique system prompts, knowledge bases, conversation styles, and branding. The cost and complexity of running individual deployments would have made the project unviable.
I designed a configuration-driven architecture where each AI agent is defined by a JSON file containing its system prompt, model preferences, temperature settings, and capabilities. A single Node.js runtime serves all agents, routing requests based on a shareable link identifier.
The platform supports RAG (Retrieval-Augmented Generation) with per-agent knowledge bases using chunked document embeddings. Responses stream in real-time. Conversation history persists per session, and agents can be updated by editing their configuration file without any code changes or redeployment.
A web UI provides the chat interface, while an admin dashboard shows usage analytics, session depths, and engagement metrics across all agents.