I've been thinking about an AI-powered workflow that takes a website from completed development to fully launch-ready, with minimal manual intervention. Imagine this: Once a website is completed, an automation built using n8n or Make kicks in and handles the entire post-development process. Here's what I'd want the workflow to automate: 1. SEO Setup - Generate SEO-friendly meta titles and descriptions. - Create XML sitemaps and submit them to Google Search Console. - Generate robots.txt. - Add relevant schema markup (structured data). - Check heading structure, image alt text, and broken links. 2. Website Performance & Security - Run Google PageSpeed Insights checks. - Check mobile responsiveness. - Scan for broken links and missing pages. - Verify SSL/HTTPS. - Check image optimisation and loading speed. 3. Analytics & Tracking - Set up Google Analytics 4. - Configure Google Tag Manager. - Add conversion tracking and important events. - Connect Google Search Console. 4. Final Quality Assurance - Automatically test important pages and forms. - Check navigation and internal links. - Generate a website audit report. - Flag issues that need human approval before launch. 5. Automated Client Handover - Deploy the website to hosting. - Generate the live website URL. - Prepare a detailed SEO and performance report. - Send the client a professional delivery email. - Share documentation, access details securely, and maintenance instructions. My question for the community: If you were building this workflow, would you use an AI agent to manage the entire process or create separate specialised automations for SEO, performance, analytics, QA, and client delivery? Also, what important post-development tasks am I missing? Would love to hear how others would architect this! 🚀

I've been thinking about how an automation can look healthy while quietly missing the work it was supposed to do. What's one simple check you use to catch a missed run?

I went through over 200 of Alex Hormozi’s videos and posts to break down how he uses AI in his business. We cover his workflows for content, sales, and customer support, why he capped his team’s AI budget, and the $350,000 automation mistake he warned about. You’ll learn how to decide what’s worth automating, where humans still do a better job, and how to aim AI at the problems holding your business back.

Something I've been thinking about is how AI agents handle data that changes while they're processing a task. For example, an agent checks a booking slot and sees it as available. Before it actually creates the booking, another process books that same slot. The agent made its decision using valid information, but that information was already outdated by execution time. I'm curious how experienced n8n builders handle this. Would you use optimistic locking, database transactions, or revalidate the resource immediately before committing the action? It seems like a small timing issue that could create some surprisingly difficult bugs.

One thing I've been thinking about is how much people rely on system prompts to control AI agent behavior. You can write detailed instructions, define boundaries, and provide examples, but what happens when the model ignores an instruction, calls the wrong tool, or produces an output that looks valid but is actually incorrect? I'm curious how experienced builders are handling this at the architecture level. A few things I'd love to understand: - Structured outputs: Do you rely on JSON schemas and strict validation, or do you add another validation layer before executing actions? - Guardrails: How do you prevent an agent from triggering sensitive actions without checking permissions or business rules first? - Tool execution: Do you let the LLM decide which tools to call freely, or do you constrain tool access based on the current workflow state? - Observability: How do you trace the model's inputs, tool calls, outputs, latency, and token usage across a multi-step execution? - Fallbacks: When the model produces an invalid response or confidence is insufficient, do you retry, switch models, or route the task to a human? My current thinking is that prompts should guide behavior, but deterministic code should enforce critical business rules. I'd love to hear how you guys approach this in production, especially when building agents that interact with CRMs, booking systems, or other external services.