What happens if an AI call goes unanswered? Until recently: Nothing. The call just failed. Now: It waits, retries, and logs everything. I recently upgraded one of my client’s AI voice agents (built with n8n + Retell AI) to make it: Smarter (knows when to retry) More reliable (safe exits, no loops) Fully trackable (logs every call & outcome) Here’s what changed 👇 ✅ Google Sheets Logging Every call—answered, missed, failed, or skipped—is stored automatically. No more digging through console logs. ✅ Retry Logic If unanswered, the system: Waits 30 minutes Redials once Tracks if the retry worked ✅ Safe Exit Path No infinite loops. A call is never retried more than once. ✅ Skipped Calls Handled Even missing info (like no phone number in a calendar invite) gets logged with clear reasoning. ✅ Future-Ready Built modular, so new logic can be plugged in anytime without breaking things. 💡 Why this matters: It’s no longer “just an AI call.” It’s a trackable, testable, and dependable system—the kind businesses can actually rely on. 🚀 If you want your AI workflows to behave like this, let’s talk. Tasknova Aarav Varma Rajpal Rathore #nocode #automation #voiceAI #RetellAI #n8n #workflowautomation #productops #retrylogic #googleapps #aiintegration #builders
Upgraded AI Voice Agent with Retell AI and n8n
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Just wrapped up a fun side project where I built a Trail Recommendation Agent using n8n 🌲 The idea was simple: Pull weather data from OpenWeatherMap Check my Google Calendar for free time Look up trails from a Google Sheet (miles, elevation, shade, time) Use an AI Agent to combine all this info and suggest the best trails for the day. Finally, send myself a daily email with recommendations via Gmail. What I loved about this: 1) n8n made it easy to connect multiple services without writing heavy glue code. 2) I got to see how triggers, APIs, and LLM prompts work together in a flow. 3) It reinforced the power of “agent thinking” → role, task, input, tools, constraints, output. For me, the most important takeaway was understanding the orchestration layer: n8n isn’t just automation, it’s a way to prototype AI agents with real-world tools quickly. #productiveSundays #n8n #GenerativeAI #LLM #NoCode #cohere #commandr
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🚀 CREATE LLM-POWERED WORKFLOWS FOR FREE WITH OPENROUTER + N8N! Here's how to get started (in <2 mins!): 1️⃣ SIGN UP AT OPENROUTER.AI • Visit OpenRouter.ai • Click "Sign Up" (no credit card needed) 2️⃣ CREATE HTTP REQUEST NODE IN N8N • Install n8n (or use cloud version) • Drag & drop HTTP REQUEST node 3️⃣ GET YOUR API KEY • In OpenRouter dashboard: Click 🔑 "Keys" • Copy your free API key (10K free tokens/month!) 4️⃣ CONFIGURE NODE SETTINGS ```JSON URL: https://lnkd.in/evxek3JC Method: POST Headers: - Authorization: Bearer YOUR_API_KEY - Content-Type: application/json Body (JSON): { "model": "google/palm-2", "messages": [{"role": "user", "content": "{{your_input}}"}] } ``` 5️⃣ TEST & DEPLOY! 👉 Attach a Manual Trigger node for testing 👉 Execute workflow → Watch AI responses flow in! PRO TIP: Use {{variables}} from previous nodes to create dynamic prompts! 🔥 BONUS: Pair this with GPT-4/Claude/Llama models (usage costs apply) ⚠️ REMEMBER: Free tier = 10K tokens/mo. Track usage in OpenRouter dashboard. 👉 YOUR TURN! Which AI automation would YOU build first? FOLLOW ME FOR MORE NO-CODE + AI MAGIC! #LLM #AI #Automation #N8N #OpenRouter #NoCode #TechTips #Productivity #APIs #FreeTools
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✨ From Prompts to Products: Lessons from Building with LLMs ✨ When I first started experimenting with LLMs, I thought the hardest part would be the model. I was wrong. The real challenge? 👉 Teaching the model to work with enterprise data (hello, RAG!). 👉 Designing UIs that make AI feel like a teammate, not a black box. 👉 Building pipelines that don’t break when traffic spikes 5× at midnight. Over the past few years, I’ve built GenAI apps that summarize legal docs, automate HR workflows, and even power real-time chatbots with <1s latency. Every project reinforced one truth: 💡 The power of LLMs lies not in what they know, but in how we connect them to what matters. Whether it’s Pinecone, FAISS, or Azure Cognitive Search — the stack matters. But what matters more is trust: reducing hallucinations, securing data, and giving users confidence in AI-driven insights. So here’s my question to you: 👉 If you had an LLM agent by your side, what task would you want it to take off your plate tomorrow? Let’s share ideas — because the future of GenAI is being built in conversations like this one. 🚀 jaganadari0825@gmail.com 8064292130 #GenerativeAI #LangChain #AIEngineering #KnowledgeSharing #Innovation
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New software + old systems = headaches? Data silos and compatibility are common hurdles. A thoughtful approach makes all the difference. Automate ops with goal-driven AI - https://lnkd.in/dx52-vzC #softwareintegration #techchallenges #businesssolutions #aiautomation #PhaedraSolutions #CustomSoftware #WebDevelopment #MobileApps #UIUXDesign #ArtificialIntelligence #MachineLearning #AIDevelopment #EcommerceSolutions #StartupTech #MVPDevelopment #DigitalTransformation #ScaleYourStartup #ProductDevelopment
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Inaugural issue of our AI Dev Tools newsletter, detailing both tools to help you in your dev workflow and tools for those building with AI. Issue #0 🔹 Warestack — Write release protection rules in plain English. No more deciphering YAML just to keep your deployments safe. 🔹 Archon — A custom knowledge base + task manager, packaged as an MCP server for your agents. 🔹 MarkItDown — Convert almost any document to clean Markdown, optimized for LLM ingestion. 🔹 TensorZero — An open-source LLM gateway with observability, optimization, and evaluation built in. 🔹 cognee — A straightforward approach to AI memory management. 🔹 Wakatime — Automatic coding analytics that show how you actually spend your time. 🔹 LangWatch — An agent simulator that helps uncover edge cases before they bite you in production. All links in comments #devtools
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🌟MCPMark: A New Benchmark for LLMs! 🌟 The MCPMark is designed to evaluate the multi-tool-use and code-related capabilities of large language models (LLMs). It features an associated leaderboard that ranks models based on their performance. 🔑 Key Aspects of MCPMark: Purpose: The benchmark aims to "stress-test" LLMs in complex contexts that require the use of multiple tools to accomplish tasks. This is especially relevant for agentic applications, where models must interact with external tools and environments to tackle real-world challenges. Developers: Created by Researchers in collaboration with Eval Sys and LobeHub. Test Data: Utilizes a high-quality dataset of 127 expert-created samples to rigorously test a model's abilities. 🎯 Focus Areas: Multi-tool Use (MCP): Evaluates how effectively models can interact with and orchestrate multiple tools to solve problems. Comprehensive Contexts: Assesses the ability to handle tasks that require more than a single-turn response. Coding: Measures performance on real-world coding tasks. 📊 How the Leaderboard Works: The MCPMark leaderboard ranks both proprietary and open-source models based on several metrics, providing a holistic evaluation: Success Rate: The percentage of tasks that the model completes successfully. Average Agent Time: The average time it takes for the model to complete the tasks. Per-Run Cost: The cost of running the model for a single task, highlighting efficiency for commercial applications and open-source development. 👇 https://lnkd.in/gpSpGpRJ #ai #agent #evals #mcp
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Most people think building AI agents requires a computer science degree. I just built 3 production-ready AI agents in n8n this weekend. Zero coding required. Here's what I learned: → Building the agent: 2 hours → Connecting to APIs: 30 minutes → Testing & debugging: 1 hour → Deploying to production: 15 minutes The game-changer? n8n's visual workflow builder turns complex AI orchestration into drag-and-drop simplicity. My 3 agents: 🚀 Automated SEO Position Tracker: It takes a list of keywords, checks their live search engine ranking, and logs the position data into a sheet. No more manual SEO checks. 📂 Smart Email-to-Cloud Organizer: It triggers on every new email, saves the content and any attachments into a unique Google Drive folder, and then updates a Google Sheet with a summary and a direct link to that folder. 🤖 On-Demand Data Scraper Bot: I send a website link to a Telegram bot, and the agent instantly scrapes the key data I need and sends it right back to me in the chat. The dirty secret: While everyone's debating LangChain vs. LangGraph, n8n users are already shipping. What's your biggest barrier to building AI agents? #AI #n8n #Automation #NoCode #AIAgents
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💡10x Productivity Tip (using AI) Spawn multiple branches for agents to work simultaneously on a single project. -> Create branches for your issues (Sentry, Linear, etc) -> Move them to different git worktrees -> Ask agents to get issue details for the connected MCP server and fix them. -> Once done, test and merge. Now you can work 10x faster than before. Try this, thank me later.
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Ever wonder why some companies see real ROI 📈 from AI while others only see surface-level results? 💡 The difference often lies between using #LLMs for text generation and deploying #AIagents that can actually take action. Agents don’t just produce words. They execute tasks, optimise processes, and create measurable impact across the enterprise. Understanding this distinction could mean the difference between experimenting with AI and truly transforming your business. 📊 Read our latest blog 👉🏼 https://lnkd.in/dEUm2uZN to see how AI agents are reshaping the future of software development. #streamliningoperations #processoptimisation #futureofsoftwaredevelopment #measurableimpact #AIagency
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What even is AI workflow automation? (My Day 0 realization) Until a week ago, the phrase “AI workflow automation” felt like something reserved for engineers. But then I realized: it’s actually simple. It’s about connecting apps so they talk to each other — instead of me doing the boring copy-paste work. Example that clicked for me: I get an email attachment. Normally, I’d download it, rename it, and save it to Google Drive. With automation, that whole process can run in the background. That’s when I thought: “Okay, this is worth learning.” And that’s how I landed on n8n as my first tool. 💬 Honest question: When you first heard “AI workflow automation,” did you get excited… or just overwhelmed? #AIworkflow #n8n #NoCodeAutomation #LearningJourney #Productivity
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