This course gave me hands-on experience with building and evaluating AI agents using LangSmith, and helped me better understand how to monitor and improve agent behavior in real-world workflows. Looking forward to applying these learnings and continuing to grow in the AI space 🚀 #LangSmith #LangChain #AIAgents #LLM #AIEngineering #Learning

In this video, we showcase how we built a fully automated AI-powered lead intelligence + sales automation pipeline at DataLinkk — designed to find, qualify, and deliver high-value opportunities in minutes. 🔹 What’s Covered 1️⃣ How the system logs into multiple platforms automatically 2️⃣ Scraping 1,000+ live projects in real time 3️⃣ Extracting key data: descriptions, emails, links & client details 4️⃣ Storing leads into a structured database pipeline 5️⃣ Using AI agents to filter, score, and rank the best opportunities 6️⃣ Pushing top leads into a live dashboard instantly This end-to-end demo shows how modern sales automation should work: ✅ No manual searching ✅ No spreadsheets ✅ No wasted outreach time ✅ Just AI-driven lead intelligence at scale 👉 Ideal for: Startup Founders Freelancers & Agency Owners Sales & Growth Teams SaaS Builders AI Automation Engineers Agentic AI & RAG Enthusiasts Enterprise Lead Generation Teams #AI #Automation #SalesEngineering #LeadGeneration #AIAgents #RAG #LangChain #AgenticAI #StartupGrowth #EnterpriseAI #DataAutomation

In this video, we break down the Supervisor pattern used in modern multi-agent architectures and show a live LangSmith simulation of how a Supervisor coordinates agents, routes tasks, and keeps complex workflows organized and efficient. 🔹 What’s Covered 1️⃣ What a Supervisor is in multi-agent systems 2️⃣ Why Supervisors are preferred over direct agent-to-agent communication 3️⃣ How task routing and decision-making work 4️⃣ How LangGraph + LangChain enable structured multi-agent orchestration 5️⃣ Live demo of a Supervisor in action using LangSmith This walkthrough shows how the Supervisor pattern simplifies coordination, control, and scalability in agentic systems while providing full observability during execution. 👉 Ideal for: AI Workflow Builders Multi-Agent System Designers LLM Application Engineers Agentic AI & Orchestration Enthusiasts Enterprise Automation Teams #AI #LangGraph #LangChain #LangSmith #AIAgents #MultiAgentSystems #AgenticAI #LLM #Automation #EnterpriseAI

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

At DataLinkk, we built a production-ready Test-Time Diffusion Deep Researcher (TTD-DR) using LangGraph and LangSmith — and ran the entire multi-stage workflow LIVE end-to-end. 🔹 Workflow Breakdown 1️⃣ Planning agent to define research strategy 2️⃣ Noisy draft generation for hypothesis exploration 3️⃣ Iterative search & retrieval across sources 4️⃣ Diffusion-style refinement to denoise insights 5️⃣ Feasibility reasoning and validation 6️⃣ Final structured research report generation This system demonstrates how test-time diffusion reasoning can be applied to deep research workflows using long-running agents, iterative refinement, and full trace visibility inside LangSmith. 👉 Ideal for: Deep Research Automation AI Agent Builders LLM Reasoning Systems Enterprise Knowledge Workflows Multi-Stage Agentic Pipelines #AI #LangGraph #LangSmith #AIAgents #DeepResearch #LLM #Automation #AgenticAI #EnterpriseAI #ResearchAutomation