Most agents = bloated prompts trying to do everything. Result? Slow, dumb, hard to fix. MCP = Modular Component Prompting. Break big tasks into small, smart agents: - Scraper → Fetch - Filter → Score - Writer → Summarize - QA → Final check Each agent = 1 job. Clean, fast, easy to debug. Like LEGO. 💡 Don’t fix the whole brain. Fix the broken piece.

AI agents aren’t just for demos or dream tools. Here’s how I use mine — every day: - 🔍 Inbox Triage Agent → Flags & replies to high-priority emails - 🧠 Idea Synth Agent → Converts rough notes into tweets or content - 📊 Client Pulse Agent → Summarizes project updates weekly - 📅 Follow-Up Agent → Reminds me who I forgot to ping All built with: MCP + lightweight logic + simple APIs No dashboards. Just smart loops that run quietly in the background. You don’t need a big launch. You need small wins, stacked daily.

Most client onboarding is just: 📎 A PDF 📎 A Calendly link 📎 A “Let’s hop on a call” But onboarding is your first product experience. Try this instead: - ✅ A single Notion page that explains everything - 🔄 Automated form → routes to right agent/workflow - 🎥 Short video that feels personal - 🤖 Light AI-powered intake (built with your stack) Clients shouldn’t feel like leads.They should feel like partners — from day one. Build it like you’re onboarding a co-founder.

Everyone’s talking about AI agents. Few are discussing how to make them work. That’s where MCP — Multi-Component Prompting — enters. Think of MCP as the brain structure behind your agent’s behaviour. Without it, your agent’s just a ChatGPT with extra steps. ✅ Let’s break it down: 🔹 MCP (Multi-Component Prompting)Instead of sending one massive prompt to the LLM, you split your task into clear, modular components like: - 🎯 Role definition - 🧱 Context memory - 🛠️ Tool usage rules - 💬 Output formatting - 🧭 Reflection or self-correction Each module feeds into the next, like an internal logic system. It’s not “prompting better.” It’s building an actual mind. 🔹 AI Agents (Autonomous + Semi-Autonomous)Agents act like mini-employees. They don’t just respond — they decide, search, loop, and sometimes even call APIs or spin up other agents. But without MCP, they become unstable: - Hallucinate steps - Misuse tools - Forget goals mid-run Pairing agents with MCP =🧠 Structured reasoning + ⚙️ Controlled autonomy = 💰 Real-world reliability ⚡ Real Example: Let’s say you’re building a Customer Support AI Agent. Here’s what an MCP-driven flow could look like: 1. Role Prompt → “You are a senior customer support rep trained in SaaS tools.” 2. Context Prompt → Pull recent customer history via API. 3. Action Prompt → Choose between: reply / escalate/ask for clarification 4. Tool Prompt → If an API call is needed, hit the endpoint with {customer_id} 5. Reflection Prompt → Was this resolved in < 3 replies? If not, summarise & escalate. You’ve now made a thinking, acting support agent — not just a chat widget. 🔁 Summary: 💡 MCP gives your agent a brain.🤖 Agents give your MCP a body.🧠 Together, they unlock use cases beyond basic bots. 👀 Want to see real MCP prompt templates or a breakdown of agent frameworks? 👉 Let me know in the comments — next post will cover: “How to Build a Reliable Agent Framework with MCP + External Tools”

Well, tech stacks can be pretty confusing if you don't know what to do with it. Here is my stack (dev tech stack) : 1. VAPI (Voice agents) 2. VoiceFlow 3. Make, Zapier, and n8n (Automation) 4. Webflow (Landing page) 5. Bubble (No code internal tool builder) 6. Claude (Basic support and prompting) 7. Few coding languages (Python, Typescript, and React) 8. RapidAPI and Hugging Face (API and open source LLM's) 9. GitHub (Open source projects ). BTW: I am working on an open-source project You don't need a big tech stack. Yeah, mine looks big, but frankly, I have a few for every project, like, like three or four depending on what I am doing