Multi-Agent AI Platform — Complete Guide

Article 98 • ARCHITECT • Module 10: Enterprise AI Projects

Why local/open-source AI matters

Multi-Agent AI Platform — Complete Guide helps teams reduce AI API cost, improve privacy, and avoid vendor lock-in by combining Microsoft AI frameworks with local model execution through Ollama.

Reference architecture

flowchart LR
  U[User] --> API[ASP.NET Core API]
  API --> ORCH[Semantic Kernel / Agent Orchestrator]
  ORCH --> OLL[Ollama Runtime]
  OLL --> M[Open Source Models]
  ORCH --> MEM[(Vector Store)]
  ORCH --> T[Tools + MCP Integrations]
  ORCH --> Q[(Queue)]
  Q --> W[Worker Agents]
  W --> OBS[AI Observability]
  ORCH --> P[Project: ERP Assistant]

Implementation sequence

1. Install and configure Ollama with selected model.
2. Set up ASP.NET Core service and model client adapters.
3. Implement Semantic Kernel orchestration.
4. Add tool/function calling and MCP interfaces.
5. Add RAG with embeddings and vector retrieval.
6. Add enterprise controls: auth, audit, prompt guardrails.

Real-world scenarios

Knowledge Assistant: secure internal docs Q&A with local inference and RAG.

ERP Copilot: domain-specific agents for HR, finance, analytics with event-driven workflows.

Security checklist

• Prompt injection filtering
• Sensitive data masking
• Per-tenant isolation
• Action allow-list for tools

Summary

This lesson translates Multi-Agent AI Platform — Complete Guide into an enterprise-ready local AI design pattern.