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The Real Secret Behind Long-Running AI Agents Isn’t Prompting AI agents are changing fast. One important idea from Anthropic’s long-running agent system is Harness Design. It is not just about giving a better prompt to the model. It is about building a proper workflow around the AI. The setup mainly had 3 roles: Planner → Converts a simple idea into a product plan Generator → Builds the application step by step Evaluator → Tests the application like a real user The interesting part is the Evaluator. It did not just check if the UI looked good. It actually tested the app using browser testing, API validation, and database checks. The workflow looked like this: Idea → Spec → Sprint Contract → Build → Test → Fix → Repeat Anthropic compared: - A single AI agent working alone - A full Planner + Generator + Evaluator setup The result was very different. The solo agent created a basic demo quickly. The harness-based system took longer, but delivered a much more complete and tested product with multiple features and proper validation. The biggest takeaway: AI engineering is moving beyond “one agent does everything.” The future is about: - Clear roles - Testing loops - Feedback systems - Reliable execution Harness Design is basically building a structured system around AI so the output becomes more dependable and production-ready. Question for everyone 👇 In this setup, which role was responsible for testing the running application and sending feedback to the builder? #ArtificialIntelligence #PromptEngineering #AIExplained #ChatGPT #AIForEveryone #TechSimplified #FutureOfWork #DigitalSkills #LearnAI #CareerGrowth #Google #Antigravity #AI #GoogleGemini #NotebookLM #LLM #GenAI #AIResearch #GoogleAI #AIBuilder #AIDevelopment

A Saturday Morning at a Tapri Changed How I See Digital India Lazy Saturday. Tapri. Chai. Half-awake. Two guys nearby — delivery team, HP Gas or Bharat Gas or something like that — were stuck on a problem. A customer had made a UPI payment. Money left the account. But nothing showing on the UPI app. Customer had a screenshot as proof. Classic panic. Back and forth they went. Customer care. Escalate. Recheck. Then one of them said: "Bhai, why don't you just check the bank statement?" The other guy looked at him like he'd said something stupid. "It's Saturday. Bank open rahega nahi rahega. And even if it is — I have to go there, stand in a queue, just to verify one transaction of ~₹1000? Leaving my work, my tasks, everything — for that?" Fair point. "Okay then — use NetBanking or the app?" "Yaar, I don't use all that. Username... password... I barely remember which email I registered with. That's too much." And right there — that's the real India. Not lazy. Not uneducated. Just a person who hasn't found a reason simple enough to cross the friction. Then came the idea. "Send a 'Hi' on WhatsApp. The bank number. Just that." He did. In under a minute — mini bank statement. Right there on screen. Transaction confirmed. Customer sorted. Problem closed. --- No app download. No password. No queue. No Saturday ruined. Just WhatsApp — a tool he already used a hundred times that day. That's what real digitalization looks like. Not the pitch decks. Not the press releases. The moment a skeptic says "oh, that's it?" and their problem disappears. --- And I'll say something here that might surprise people who know me: The foundation of this was laid by Congress. BJP took it forward. Both deserve that credit. I lean left. I criticize what I think is wrong — and I'll keep doing that. But I'd be dishonest if I didn't acknowledge what's genuinely been built here. India's UPI and digital banking infrastructure is world-class. A common man at a tapri on a Saturday morning proved it — without even trying to. #DigitalIndia #UPI #Fintech #BankingForAll #IndiaGrowth #LinkedInIndia

🚨 Most AI teams are using the wrong RAG architecture, and it’s silently killing accuracy. Everyone says “we have RAG.” But how your RAG works determines: → Accuracy → Cost → Latency → And whether your system can actually reason Let’s break it down simply 👇 🧠 1. Classic RAG — “Retrieve & Hope” → Query → Embed → Vector Search → Top-K → LLM answer ✔️ Fast, cheap, easy to ship ✔️ Great for basic Q&A ❌ Struggles with multi-hop questions ❌ Fails when context is scattered ❌ No real reasoning 👉 It’s basically: “find similar chunks and hope they make sense together” 🕸️ 2. GraphRAG — “Connect the Dots” → Query → Entity extraction → Graph traversal → Connected nodes → LLM answer ✔️ Understands relationships (people, events, concepts) ✔️ Strong for multi-source reasoning ❌ Higher setup cost ❌ Needs knowledge graph design + maintenance 👉 Best for: Legal • Biomedical • Compliance • Enterprise knowledge systems 🤖 3. Agentic RAG — “Think Before You Answer” → Query → Agent decides → Multi-step retrieval → Self-check → Iterate → Final answer ✔️ Decides WHAT to retrieve ✔️ Decides WHEN to retrieve ✔️ Decides IF the answer is good enough ✔️ Can combine: Vector search + Graph + APIs + Web + Tools ❌ More latency ❌ More tokens ❌ Harder to debug 👉 But: Highest accuracy for complex questions ⚡ The real difference? Classic RAG → Retrieves GraphRAG → Connects Agentic RAG → Reasons 🏗️ What winning teams are doing in 2026: Not choosing one — but combining all three: → Classic RAG = fast path → GraphRAG = relationship-heavy queries → Agentic layer = intelligent routing 👉 The future is Hybrid RAG systems 💬 Curious: What RAG setup is your team running in production right now — and where does it break? #AI #GenAI #RAG #AgenticAI #GraphRAG #LLM #AIArchitecture #MachineLearning #TechLeadership