Built a Trail Recommendation Agent with n8n and AI

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

AI agents don’t get powerful by adding more prompts. They get powerful when the system around them is designed right. - Memory to keep context across tasks - Tools to fetch real-time data - Orchestration to decide which step runs next - Guardrails to stop bad outputs That’s when an “LLM that talks” becomes an agent that works.

🌟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

Behind every successful automation is a strong technical foundation. When building with n8n + AI, a few often-overlooked factors make the difference: - Webhooks vs Polling — Using webhooks reduces API load and ensures real-time data flow. - Retry Logic — External APIs fail sometimes; workflows need smart retries, not silent breaks. - Data Mapping — Consistent schemas keep downstream tools accurate and prevent reporting errors. - Scalability — Design workflows to handle 100 tasks today and 10,000 tomorrow. Automation isn’t just about connecting tools — it’s about engineering systems that are reliable, scalable, and future-proof. #AI #Automation #n8n #TechForBusiness #DataEngineering #WorkflowAutomation

2024 was the year of experimenting with AI agents. 2025 is the year of building real tech stacks that can scale. Here’s a look at the emerging AI Agent Tech Stack that’s shaping the ecosystem: 1. Frameworks @pyautogen → AG2: end-to-end multi-agent automation @crewAIInc → simple, fast, powerful multi-agent framework @LiteLLM → call 100+ LLMs with a single library 2. Monitoring @AgentOpsAI → observability for AI agents 3. Search @firecrawl_dev → convert websites into LLM-ready data @perplexity_ai → AI-powered search engine @ExaAILabs → enterprise-grade search & crawling 4. External APIs @composiohq → 250+ tools to connect with agents @stripe → usage-based billing for AI agents 5. Computer Use @browserbasehq → browsers built for agents @OpenInterpreter → give agents terminal control 6. Memory @mem0ai → advanced memory management @neondatabase → serverless Postgres with RAG baked in The ecosystem is expanding fast. The real question is: Which of these will drive the biggest breakthroughs in 2025? #AI #Agents #TechStack #2025 Powered By PixelPie Media Prashant Jhakarwar

Why use different models in the same conversation? Specialization wins: One model can brainstorm brilliantly, another can verify math/code, a third can summarize fast. Cost + speed control: Route routine steps to lighter models and reserve premium models for hard reasoning. Reliability: If one model stalls or drifts, a second opinion catches errors (ensemble-style prompting). Continuity: Keep context in one thread while swapping the “player” best suited for each step. How it works: Role routing: Creative → “writer” model; analysis → “analyst” model; cleanup → “editor” model. Guardrails: Use a factuality checker or citation bot to vet outputs before they reach users. Memory handoff: Pass condensed summaries between models to keep the chat lean and coherent. Custom models & OpenWebUI Your data, your edge: Fine-tune an open model on domain docs (policies, product manuals, codebase). Import path: With OpenWebUI (and similar local runtimes like Ollama), you can load your custom model and plug it into your workflow—great for privacy, rapid iteration, and avoiding per-token costs. Best of both worlds: Pair your custom specialist with a generalist cloud model for tough reasoning, then let a lightweight model handle high-volume tasks. Decompose the task into ideate → analyze → verify → format. Assign a model per stage (e.g., creative LLM → code/math LLM → checker → fast formatter). Cache + summarize between stages to cut latency and spend. Measure: track quality, speed, and cost—promote/demote models based on results. Where 3ns.domains helps: Orchestrating multiple models, and a custom one from OpenWebUI, can get messy. 3ns.domains helps you evaluate, route, and connect the right mix (cloud + local + custom) in a single flow. That means less glue code, cleaner handoffs, and a faster path from idea to production. Don’t bet everything on one model. Build a team: specialist + generalist + your custom expert. Let platforms like 3ns.domains keep the whole conversation running smoothly. #AI #LLM #GenAI #MLOps #Productivity #OpenSource #OpenWebUI #3nsdomains

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

🚀 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

𝗜 𝘀𝗽𝗲𝗻𝘁 𝗰𝗼𝘂𝗻𝘁𝗹𝗲𝘀𝘀 𝗵𝗼𝘂𝗿𝘀 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝘃𝗼𝗶𝗰𝗲 𝗔𝗜 𝗳𝗹𝗼𝘄𝘀 𝘀𝗼 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼. Here are 3 tips to cut latency and make your agent feel human: 1️⃣ Logic before AI. Validation, routing, and data handling don’t need AI. Hard rules respond in ~1 second — AI only steps in when logic can’t. 2️⃣ Choose the right tool. Your infra makes or breaks the experience. Tools like Vapi, Pipecat, LiveKit, or Retell keep response times low (~500ms) and conversations smooth. 3️⃣ Keep prompts tight + structured (Markdown format). AI doesn’t need essays. Short, clear, well-structured prompts mean the model knows what to do, when to do it, and what comes next — without wasting time. Always remember: ⚡ Speed is trust. If your agent drags, the caller leaves — no matter how “smart” the tech looks. 👉 Builders, what’s your golden rule for keeping latency under control? #VoiceAI #AIagents #Automation #Latency #FutureOfWork #n8n

🚀 New releases alert! We're excited to share the latest updates to our any-suite tools! any-llm v0.15.0 ✨ What's new: List Available Models - Easily discover and access all models that a provider supports Local Demo Chat Application - Test different models with our new locally runnable chat application Enhanced Async Support - Better performance for asynchronous operations Expanded Provider Support - Now supporting llama.cpp and Perplexity any-agent v1.5.0 🔧 Key update: Any-llm support in agent frameworks - When using the tinyagent framework, any-llm is now the default library for LLM provider communication. Check out the repos:  🔗 any-llm: https://lnkd.in/eTMjFPV4 🔗 any-agent: https://lnkd.in/dN399CQw 🙏 Huge thanks to our contributors for making these releases possible: @mihirahuja1, Pankaj Bind, Anivar Aravind, @inventivepotter, @Shiva4113, and @ELC! Your contributions keep the any-suite moving forward. Looking to contribute? Take a look at our updated contribution guidelines: https://lnkd.in/esjai8x4 #AI #MachineLearning #OpenSource #LLM #DevTools #ArtificialIntelligence