Webhooks changed how I think about AI agent automation. Before webhooks, my automations were all time-based: check for new emails every 5 minutes, scan for GitHub notifications every 10 minutes, poll the server status every hour. With webhooks, the events come to me. No polling. No delays. No wasted API calls checking when nothing has
If you’re new to deploying AI agents and the terminology is overwhelming, this is the article I wish I’d had when I started. No assumptions about your background. Just the tools, the concepts, and the steps.
What “Deployment” Actually Means
Deployment is getting your AI agent from “running on my computer” to “running on a server
Choosing AI for workflow improvements makes sense when the tasks involve natural language understanding, contextual decision-making, or content generation. It doesn’t make sense when the tasks are purely mechanical, perfectly predictable, or safety-critical.
Choose AI When:
Input is unstructured. Emails, chat messages, documents, social media posts. AI excels at understanding the intent behind varied human language.
When I started with OpenClaw, I spent days searching for “the complete list of everything this platform can do.” I found blog posts, GitHub repos, Discord threads, YouTube tutorials — scattered across dozens of sources, most of them incomplete or outdated.
So I made the map myself. Everything I’ve found in eight months of daily use,
Deploying an AI agent step by step — from local development to a running production service. No abstractions, no “it depends,” just the actual commands and decisions.
Step 1: Get a Server
You need a Linux server with at least 2GB RAM (4GB recommended), a public IP address, and SSH access. A $10-20/month VPS from any
The AI agent needs to send emails. Not forward them, not summarize them — actually compose and send them based on context. A customer emails about a delayed order? The agent should check the order status, draft an appropriate response, and send it (or queue it for my approval).
Building this took me three iterations. The
Every commit I push now gets reviewed by an AI before any human sees it. Not because I don’t trust my own code — because I don’t trust my own attention at 11 PM on a Friday when I’m trying to ship a fix before the weekend.
The AI reviewer catches things I miss: a variable
Scaling an AI agent from serving 1 user to serving 100 introduces problems you don’t see at small scale. Here’s what breaks first and how to handle it.
What Breaks at 5 Users
Context collisions. Multiple users sending messages simultaneously, and the agent confuses who said what. Fix: isolated sessions per user. Each user gets their
Every week, someone in the OpenClaw Discord asks: “Should I use OpenClaw or LangChain?” And every week, the answer is the same: “They’re not really competing products.” But nobody explains why clearly, so here’s my attempt.
I’ve used both extensively. LangChain for about a year, OpenClaw for eight months. I’ve built production systems with each. And
