🧠 Orchestrator Agent Design Summary Author: Rain Lei Objective: To design a reusable, configurable, and intelligent orchestrator agent system within AI Workspaces that supports routing, workflow design, and agent communication. ⸻ 🔧 1. System Components Overview Component Description Frontend UI Visual drag-and-drop interface for designing agent workflows; supports reuse and modularity Orchestrator Agent Central agent responsible for task orchestration, routing, state management Router Agent A lightweight version of the orchestrator; focuses purely on task routing A2A Protocol Agent-to-Agent communication protocol to support message passing and shared context Agent Registry Use add_agents() to register available agents into the AI Workspace Workflow Reference Tools UI/UX inspiration from N8n, Flowise for designing logic flows ⸻ 📘 2. Problem-Oriented Design Strategy 🧠 Architecture & Design Problem Proposed Solution Is the current architecture too generic to support task-specific agents? Introduce Orchestrator Agent that dispatches to specialized agents; UI allows selection and reuse of specific agents Do we need a Router Agent? Yes, it can serve as a minimal viable orchestrator for routing and simple delegation How can the system determine which agent to call? Use ReAct-style prompt logic:Thought → Action → Observation → Final Answer, combined with tool/action selection Can the LLM autonomously plan workflows in the future? With frameworks like Autogen or Google ADK, agent-chain execution is feasible and improves over time ⸻ 🤖 Multi-Agent & Workflow Problem Proposed Solution How do agents collaborate effectively? Implement A2A protocol and shared execution context (memory/state) How do we make agents reusable? Register all agents using add_agents() with metadata and configurable parameters How to build workflows visually? Use drag-and-drop interfaces (like N8n) to visually connect agents into workflows How to execute complex agent chains? Leverage agent-chaining frameworks (e.g., Autogen, ADK) that support conditions, loops, tool-calling, memory ⸻ 📈 Observability & Logging Problem Proposed Solution How to trace agent execution? Use structured logging for every step: Thought, Action, Input, Output (agent scratchpad style) How to standardize agent logs? Define on_call_log() hooks for each agent to output unified logs for inputs, decisions, outputs ⸻ 🔧 Product & Delivery Collaboration Problem Proposed Solution How can non-technical users set up workflows? Frontend UI with form-based config for agent params, and no-code flow design What agent framework should be used? ✅ Google ADK is recommended due to strong ReAct support and easy agent registration✅ Autogen also works well for more advanced cases❌ LangGraph is not suitable due to lack of A2A (no agent-to-agent communication) ⸻ 👥 Human Trust & Human-in-the-Loop Problem Proposed Solution How to build trust in automation? Inject “human approval nodes” into the workflow where users validate intermediate outputs How to handle bad input data? Let Orchestrator handle fallback logic or call validation agents before executing downstream agents ⸻ 🧩 3. Framework Comparison Framework Recommended? Notes Google ADK ✅ Highly recommended ReAct-style, tool-call friendly, good for workspace agents Autogen ✅ Recommended Suitable for chaining multiple agents and handling dialogue coordination LangGraph ❌ Not recommended Graph-focused but lacks inter-agent communication features (no A2A) ⸻ 📝 4. Next Steps 1. Build the visual workflow editor (based on N8n-style flow canvas) 2. Design the agent registry system and interface contract (add_agents()) 3. Define A2A protocol and agent memory/context model 4. Implement standard logging and observability framework 5. Deliver a lightweight Router Agent MVP for testing basic routing 6. Embed ReAct Prompt structure as the reasoning backbone for orchestration