We Have Built an Advanced Multi-Agent LangGraph System with Planners & Human-in-the-Loop for Salesforce

We Have built a production-style multi-agent system that demonstrates how modern agent architectures can be planned, controlled, and executed end-to-end using LangGraph.

🔹 Workflow Breakdown

1️⃣ We use a supervisor agent to analyze user queries and intelligently route tasks

2️⃣ We designed two specialized sub-agents, each focused on a different domain

3️⃣ We implemented independent planners to generate structured plans per agent

4️⃣ We added a Human-in-the-Loop approval gate (Approve / Edit / Reject / Re-plan)

5️⃣ We integrated MCP (Model Context Protocol) with SSE-based automatic tool loading

6️⃣ We execute plans dynamically only after human validation

7️⃣ We return final structured outputs from the correct sub-agent

This system shows how we combine transparency, control, and reasoning in complex multi-agent workflows using LangGraph.

🔹 System Capabilities

• We coordinate agents through a supervisor-driven architecture

• We enable planner → agent routing logic

• We keep humans in control of the planning pipeline

• We support automatic tool discovery via MCP

• We perform tool-calling inside agent reasoning loops

• We maintain conversation memory, checkpointing, and error handling

• We provide full end-to-end execution visibility

🎥 What You’ll See in This Demo

• We walk through the full planning sequence

• We show supervisor reasoning and planner selection

• We demonstrate the human approval and re-planning step

• We execute tools via MCP/SSE

• We run sub-agent workflows

• We deliver the final end-to-end result

👉 Ideal for:

Multi-Agent System Design

LangGraph & LangChain Developers

Human-in-the-Loop AI

Enterprise AI Workflows

Salesforce Automation

Agentic AI Architectures

#AI #LangGraph #LangChain #MCP #AIAgents #HumanInTheLoop #AgenticAI #EnterpriseAI #MultiAgentSystems #Automation #Salesforce #LLM

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