I built a WhatsApp AI agent by prompting Claude

Disclosure: I'm on the Zernio team, so the build below uses its MCP server. But the MCP pattern works with any tool that exposes a server, so this is more about the approach than the pitch. The thing that made me actually post this: with MCP, the agent doesn't just use a WhatsApp API, it builds the bot for you. **What you can build this way:** * Support bots that answer from your docs * Lead qualification that reads an inbound chat and routes it to your support/sales team * An AI agent that replies on your own LLM key, then hands off to a human when it's stuck Basically anything where someone messages your WhatsApp number and you want some logic to run before (or instead of) a person. **The setup is genuinely this short:** 1. Connect your WhatsApp Business number to Zernio (under a min, runs on the official WhatsApp API) 2. Add the Zernio MCP server as a connector in Claude (or your LLM of choice) 3. Tell it what you want, plainly: *"answer inbound WhatsApp messages from our docs, hand off to a human if you can't help"* 4. The agent builds the flow: inbound WhatsApp message → AI (your key) → check → handoff, and publishes it to a real number 5. You text the number. It answers. **So what it actually costs to run:** 1. The builder - $0. The Zernio workflow is included in the plan. 2. Phone number - $0 - \~$2/mo for most countries (connect your own number, or buy one for the bot to answer on). 3. WhatsApp messages (Meta) - a bot replying to inbound messages pays Meta $0 for those replies, as long as they're inside the 24-hour window after the customer wrote in. You only pay when you start the chat with a template (marketing \~$0.01–0.14/msg, utility much less, varies by country). 4. LLM tokens - paid to your model provider directly. So a support bot answering inbound messages is basically: the number, plus pennies in tokens. Do you let AI handle most of it, or keep it simple and lean on human handoff?