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Stop building AI Agents for everything. Most people jump from: Agents → Tools → Frameworks → Chaos Without ever asking: Do I even NEED an agent? Effective AI isn’t about building more. It’s about building the right things. Real skill = Agent Decision Thinking Foundations → Logic → Tools → Workflows → Multi-Agent → Eval → Build Save this before over-engineering!!! 📌 Comment “AGENTS” for Agents learning hub !

(Save to Revisit Later) ⤵️ 1) Agent Control Room for AI Agents Build a dashboard that records every agent run step by step: what it was asked, what tools it used, what data it touched, what failed, and how much it cost. It should also let you replay failures so you can debug agents like real software. Tech stack: FastAPI, Postgres, Redis + Celery, OpenTelemetry, Next.js dashboard, Ragas or DeepEval for quality checks. 2) Guardian Agent that Supervises Other Agents Build a supervisor that watches an agent in real time and blocks risky actions like accessing sensitive data or sending something externally. If risk is high, it asks for approval or rewrites the action safely. Tech stack: FastAPI, Postgres, Redis streams or Kafka, policy rules (JSON/DSL), LLM for risk scoring, DeepEval for compliance tests. 3) Feature-Level AI ROI Tracker Build a tool that links AI cost to product features and user outcomes. It shows which features burn tokens and which ones actually improve retention, then auto-downgrades models or limits usage for low-ROI features. Tech stack: PostHog or Segment, FastAPI, Postgres/ClickHouse, dashboard (Metabase/Next.js), model policy router. 4) Multimodal Document Intake for PDFs, Scans, and Tables Build an intake pipeline that reads PDFs and scans, extracts structured fields, preserves tables, highlights sources, and creates a review pack when confidence is low. Built for insurance, healthcare, and banking workflows. Tech stack: FastAPI, pypdf + OCR, multimodal extraction model, Pydantic schemas, S3 storage, Postgres, review UI (Bonus) ✨ 5) Evaluation-First Testing Harness for RAG and Agents Build “unit tests for AI apps” so every change to your RAG or agent flow gets tested before production. You create a test set of real queries, expected behaviors, and failure rules, then the harness runs automated evaluations like grounding, correctness, refusal behavior, tool accuracy, and hallucination risk. If quality drops, the system blocks the release and shows exactly what changed and where it broke. Tech stack: Python runner, Ragas or DeepEval, FastAPI, Postgres, GitHub Actions, simple results dashboard.

Company hiring AI agent for $10,000 a month. RevenueCat just posted a job listing that humans literally cannot apply for. It's for an autonomous AI agent to join their team. Here's what they want: • Write 2 blog posts per week • Run growth experiments • Engage with developer communities • Work directly with their product teams The application process: • Agent must submit its own application • 48-hour take-home assignment • Multi-stage interviews with real humans • Panel reviews and founder interview This is a real company with 120+ employees paying $60,000 over 6 months for an AI agent to do actual work. This isn't a thought experiment or marketing stunt. It's happening right now, and it feels like we're watching the future of work unfold in real time. Hashtags: #AI #AIJobs #FutureOfWork #RevenueCat #AIAgent

AI agents hide a career cost nobody warns you about. Comment A or B: keep judgment, or outsource it. #ai #productivity #career #reels #usa

Meet Airia 🤖✨ an AI agent platform that helps you automate your job search, applications, and more You can turn it into a job-search AI agent 💼⚙️: upload your CV, set rules for skills, salary, and location, and your agent finds matching job roles every day. You can even automate applications. And this is just one agent 👀 Airia lets you build unlimited AI agents, plus access 2,500+ prebuilt agents 🧠📦 you can use or clone for different workflows. If you’re serious about landing interviews faster in 2026, this is worth trying. Comment LINK 🔗 and I’ll send you the free trial.

Stop babysitting AI like it’s 2023. Be honest—how much time do you spend: • Rewriting prompts • Double-checking outputs • Copy-pasting results • Repeating the same task tomorrow That’s big supervision vibes. You’re treating AI like an intern who needs constant oversight. The businesses actually winning are making AGENTS. They’re not prompting. They’re building agents that run on their own. The difference: ❌ Babysitting AI – Prompt every task – Check every output – Execute every step – Repeat daily ✅ Autonomous AI – Triggers automatically – Decides what to do – Executes without you – Runs while you sleep One needs babysitting. The other builds leverage. I made a free AI Agent Workflow Mapper that shows you: ✓ Which tasks are eating your time ✓ Which ones follow patterns ✓ Which ones can be handed off to an agent ✓ What babysitting them is costing you 20 minutes. Fill it out. Stop babysitting. Link in bio

BIG MISTAKE 🚨 I could have wasted 90 days building the wrong AI agent Here’s what happened: Day 1: I jumped straight into executive recruiting agents. Sounded profitable. Big numbers. $75K placements. But the Problem was I’ve never worked with recruiters. I don’t know their world. I was just guessing Until the light bulb moment hit me today: Why am I building for an industry I don’t understand? So I stopped. Took a step back. And used a framework that would’ve saved me weeks. The 3-Step Framework (Save This): 1️⃣ Reverse Engineer Your Network → Pick an industry you know or have contacts in → Ask real people about real problems 2️⃣ Stalk Their LinkedIn → See what they engaged with before finding you → Understand their problem awareness level 3️⃣ Understand Business Context → Company size, funding, revenue → Match your pricing to their reality Context = everything. THE BRUTAL LESSON: I was so excited to start building that I skipped the research. I chose recruiters because AI suggested and the numbers looked good on paper. The best AI agent ideas aren’t in industries with big numbers. They’re in industries YOU already understand, with people YOU can already reach, solving problems YOU’VE already seen Start with what you know. I’m updating my entire approach. New industry. New agent. Same 90-day deadline But this time, I’m building something I actually understand! Lesson learnt 🙇‍♀️ Sharing it so it saves you months. Day 4/90 ✅ Reset. But better positioned than before

Comment “LINK” if you want access to the documentation of this entire process Day 1. This is either genius or I’m just being a dumbo on the internet While everyone’s building AI agents for startups paying $100/month, I’m building mine for executive search firms that charge $75,000 per placement My AI agent - Maps candidate history - Identifies probability of them switching companies - Scores GitHub commits (for tech roles) - Writes personalized outreach sequences - Syncs to their CRM automatically They wake up to 50 vetted candidates Currently costs them: $60K/year researcher My agent: $2-3K/month The problem? They probably won’t buy it. Not because it doesn’t work. Because they don’t trust AI to do “their job.” So I have 90 days to prove an AI agent can recruit executives better than a human If I’m right: $10K/month in 90 days If I’m wrong: I just publicly wasted 3 months Day 1/90 ✅ Comment SMART if you think this will work Comment DUMB if you think I’m delusional

Comment “Kortix” for the AI Builder 🔥 #aiagents #aiagent #n8n #n8nworkflow #n8nautomation Kortix AI Agent Builder, Free AI Builder, AI Agent Trends 2026, AI Agent Automation, AI Agent Types, AI Agent Solutions, AI Agent and Automation, AI Agent for Business

Stop being confused by the jargon. ✋ AI Agents aren’t just “smarter chatbots” they’re AI with a to-do list and a brain. Think of it like this: ❌ Chatbot: Gives you a recipe. ✅ Agent: Goes to the store, buys the ingredients, and cooks the meal. An Agent uses Workflows to stay on track and Actions to get things done, but the magic is that it actually THINKS through problems instead of just breaking when things change. 🧠 #aiagents #aiexplained #futureofwork #ai

LinkedIn is changing the game again! 🚀 This time, they’ve rolled out the Hiring Assistant—a full-scale AI agent designed specifically for recruiting. Here’s what it actually does: ✅ Sourcing on Autopilot: It finds your ideal candidates for you. ✅ Instant Screening: Analyzes applications in a matter of seconds. ✅ Personalized Outreach: Crafts tailored messages that actually get replies. LinkedIn is no longer just a social network—it’s an intelligent partner that handles 80% of your busywork. Check out the video for the full breakdown! 👆 #LinkedIn #AI #Recruiting #AIagents #HiringAssistant FutureOfWork HRTech

AI agents can now hire real humans to do physical work for them 💀 That's the idea behind RentAHuman.ai, a service where people list their skills, location, and hourly rate, and autonomous software can book them for tasks that need a real person in the world ⚡️ New agent systems like OpenClaw, previously known as Clawdbot, recently went viral for carrying out digital tasks on their own, showing how capable autonomous AI is becoming. But even these agents hit the same wall. They cannot step outside, talk to someone face to face, pick something up, or check something physically 🚨 That's where this comes in. Tasks can include running errands, attending meetings, taking photos, signing papers, or verifying something on site 🔥 The system connects agents and humans through APIs, and once a task is completed, the person gets paid. It bridges the gap between what AI can plan and what still requires a physical presence 💻 This flips the traditional gig economy. Instead of humans using apps to find work, AI agents are now the ones hiring and managing human labor 🤯 What are your thoughts on this? Drop it below 💬👇 Follow @scalebysarthak for AI updates that reshape how work gets done 🎯 #rentahuman #aiagents #gigeconomy #futureofwork #automation