Multi-Agent Orchestration in .NET Using A2A

A developer demonstrated how to orchestrate a multi-agent system in .NET using the Agent-to-Agent (A2A) protocol, converting remote agents into AIFunction tools for an LLM-based orchestrator. The proof-of-concept dynamically registers agent cards, enabling parallel calls to specialized agents like an assortment specialist and a supply chain agent without code changes.

Investigation of how to orchestrate an agentic system using an A2A protocol based on .NET primitives and create a PoC. Use Case : We have existing agents that support A2A, and we want to build it into a multi-agent system. Let's start from theory. The agent-to-agent A2A protocol is designed to define well-known contracts for communication between agents without humans. Every agent publishes an AgentCard /.well-known/agent-card.json . An Agent client discovers the card, then sends tasks to the agent as messages. Our building blocks: php graph LR User User -- Orch Orchestrator Orch -. discover AgentCard .- A Assortment agent Orch -. discover AgentCard .- S SupplyChain agent Orch -- A2A SendMessage -- A Orch -- A2A SendMessage -- S A -- AT Catalog tools S -- ST Stock tools IChatClient Azure OpenAI, Bedrock, OpenAI, etc. . Microsoft provides an abstraction for the A2A spec for ASP.NET Core js var agentCard = new AgentCard { Name = "AssortmentSpecialist", Description = "Handles shop inventories, product categorizations, catalogs, and store assortments.", Skills = new AgentSkill { Id = "get-product", Name = "GetProduct", Description = "Look up a product's SKU, category, active status, and store coverage by name.", }, , }; builder.Services.AddA2AAgent<DomainAgentHandler agentCard ; var app = builder.Build ; app.MapWellKnownAgentCard agentCard, "" ; app.MapA2A "/" ; The handler processes tasks using the LLM and the agent's own tools. public async Task ExecuteAsync RequestContext context, AgentEventQueue eventQueue, CancellationToken ct { var responder = new MessageResponder eventQueue, context.ContextId ; var messages = new List<ChatMessage { new ChatRole.System, "You are the Assortment specialist. Use the tools to look up real data." , new ChatRole.User, context.UserText ?? string.Empty , }; var options = new ChatOptions { Tools = AIFunctionFactory.Create tools.GetProduct }; var response = await chatClient.GetResponseAsync messages, options, ct ; await responder.ReplyAsync response.Text, ct ; } There are several ways to orchestrate agents We chose the last one because we want to have the possibility to add a new agent without any code changes. That way, we register every agent card dynamically as an orchestrator agent tool, and aggregation happens in the same LLM loop. public async Task<string HandleAsync ChatThread thread, string userMessage, CancellationToken ct { var agents = await registry.GetAgents ct ; var tools = agents.Select ToTool .Cast<AITool .ToList ; var messages = new List<ChatMessage { new ChatRole.System, SystemPrompt }; messages.Add new ChatMessage ChatRole.User, userMessage ; using var client = new FunctionInvokingChatClient chatClient { AllowConcurrentInvocation = true, }.AsBuilder .Build ; var response = await client.GetResponseAsync messages, new ChatOptions { Tools = tools, AllowMultipleToolCalls = true }, ct ; return response.Text; } We convert remote agents to AIFunction from AgentCard js private AIFunction ToTool RemoteAgent agent { var dispatch = async string request, CancellationToken ct = { var response = await agent.Client .SendMessageAsync request, Role.User, cancellationToken: ct ; return ExtractText response ; }; return AIFunctionFactory.Create dispatch, agent.Card .Name, $"Ask the {agent.Card.Name} specialist. {agent.Card.Description}" ; } The orchestrator resolves each card with A2ACardResolver, then turns it into a tool. A request that needs both agents makes the LLM call both agents in parallel and merge their replies into one. sequenceDiagram actor User participant Orch as Orchestrator LLM loop participant A as Assortment agent participant S as SupplyChain agent User- Orch: "Stores carrying the coat AND its stock?" par send both messages in parallel Orch- A: A2A SendMessage sub-task and Orch- S: A2A SendMessage sub-task end A-- Orch: catalog answer S-- Orch: stock answer Orch- Orch: merge results Orch-- User: one cohesive answer A .NET 10 PoC for the A2A. Orchestrator discovers agents over HTTP, exposes each as a tool to one LLM loop, and aggregate to one response. All LLM inference runs locally through Ollama llama3.2 . graph TB Ollama "Ollama<br/ llama3.2<br/ local LLM external " User User / Browser -- |HTTP| Orch subgraph Orch "Orchestrator" API "Minimal API + chat UI<br/ /api/chat" Svc "OrchestrationService<br/ one tool-calling LLM loop" Reg "AgentRegistry<br/ AgentCards + A2AClients " Store "ChatStore<br/ history by threadId " API -- Svc Svc -- Reg Svc -- Store end Svc -- |LLM: tool loop + synthesis| Ollama subgraph Assort "AssortmentSpecialist A2A server " AH "DomainAgentHandler" AT "AssortmentTools<br/ GetProduct / GetActiveCatalog" AH -- AT end subgraph Supply "SupplyChainAnalyst A2A server " SH "DomainAgentHandler" ST "SupplyChainTools<br/ GetStock / GetShipments" SH -- ST end Reg -.- |discover AgentCard| Assort Reg -.- |discover AgentCard| Supply Svc -- |A2A SendMessage / tool call| Assort Svc -- |A2A SendMessage / tool call| Supply AH -- |LLM: tool-calling| Ollama SH -- |LLM: tool-calling| Ollama See docs/architecture.md https://github.com/ohalay/a2a-poc/docs/architecture.md for diagrams and the full request flow, and AGENTS.md https://github.com/ohalay/a2a-poc/AGENTS.md …