Series
Building Multi-Agent AI Systems
12 posts filed under Building Multi-Agent AI Systems.
Building a Multi-Agent E-Commerce Platform: The Complete Guide
Start here. A guided index to 40 chapters on building production multi-agent systems with Microsoft Agent Framework, in Python and C#, against one open-source e-commerce platform.
AI Agents: Concepts and Your First Implementation
What AI agents actually are, how they differ from chatbots, and a hands-on walkthrough to build your first agent with Microsoft Agent Framework.
Prompt Engineering for AI Agents -- Grounding, Roles, and YAML Configuration
How to write agent prompts that prevent hallucination, adapt to user roles, and stay maintainable -- with YAML-based configuration you can version-control.
Building Domain-Specific Tools -- Giving Agents Real Capabilities
How to build production-quality agent tools that query databases, validate business rules, and automatically scope data to the current user.
Multi-Agent Architecture: Orchestration and the A2A Protocol
How to build an orchestrator that routes requests to specialist agents, and the A2A protocol that makes agent-to-agent communication standardized.
Observability -- Tracing Multi-Agent Workflows with OpenTelemetry
How to instrument multi-agent systems with OpenTelemetry — auto-instrumented traces across agents, LLM calls, and database queries.
Frontend: Rich Cards and Streaming Responses
Transform agent text responses into interactive product and order cards, then add token-by-token streaming via SSE — no backend agent changes needed.
Production Readiness: Auth, RBAC, and Deployment
JWT authentication, role-based access control, and user-scoped data isolation for a multi-agent system, plus a one-command Docker Compose startup.
Agent Memory -- Remembering Across Conversations
Give your agents persistent memory -- store user preferences, recall past interactions, and personalize responses across conversations.
Evaluating Agent Quality -- Testing What You Cannot Unit Test
Build a repeatable evaluation pipeline for multi-agent correctness -- golden datasets, automated scoring, and CI/CD integration.
MCP Integration -- Connecting AI Agents to the Tool Ecosystem
Replace hand-coded tools with Model Context Protocol servers -- standardized tool discovery and execution for any agent framework.
Graph-Based Workflows -- Beyond Simple Orchestration
Move from LLM-driven routing to deterministic graph-based workflows -- sequential pipelines, parallel execution, conditional branching, and state management.
