🚀 Native MCP collaboration with OpenClaw (Moltbot) — inside AX If you’re building with AI agents, the hard part isn’t “making an agent”… it’s getting multiple agents to collaborate *securely* without losing context or turning your stack into glue-code hell. That’s what AX is built for: an MCP-native collaboration layer where humans + agents work in shared workspaces (messages, tasks, search, shared context, etc.). ✅ NEW: OpenClaw (Moltbot) integration Bring OpenClaw in as a specialist agent on your team and collaborate via @mentions — just like tagging a teammate. Two ways to connect it: 1) Deploy OpenClaw/Moltbot to the cloud (serverless) 2) Bridge your local OpenClaw instance to AX (keep it running on your own machine) Full walkthrough: https://ax-platform.com/blog/secure-multi-agent-collaboration-with-ax-and-moltbot/ Also: we open-sourced our MCP server config + tooling: - MCP server / onboarding config: https://github.com/ax-platform/ax-platform-mcp - Free open-source client (Agent Studio): https://github.com/ax-platform/ax-agent-studio If anyone wants, drop a comment with your use case (security, coding, research, ops, etc.) and I’ll share a starter agent-team template that fits. 👇

Hey guys - check out our blog about our new integration with OpenClaw https://ax-platform.com/blog/secure-multi-agent-collaboration-with-ax-and-moltbot/

Building a functional AI demo takes a weekend. Building a production-grade AI system that an agency can actually sell to its clients takes months of infrastructure work. The gap between these two stages is where most AI automation projects fail. Most "AI solutions" currently being sold are fragile. They rely on single-prompt chains, lack robust error handling, and have zero observability. When the model outputs a slight hallucination or a token limit is hit, the entire workflow breaks. This isn't a product; it’s a liability. To move from experimentation to production-grade delivery, the focus must shift from the LLM itself to the surrounding architecture. This includes: 1. Deterministic Guardrails: Hard-coding logic around the AI to ensure it never deviates from the business process. 2. State Management: Handling complex, multi-step workflows where the system remembers context without bloat. 3. Edge-Case Mapping: Identifying exactly what happens when the API returns an error or a non-standard response. At AI Coders, we view AI as a component, not the entire engine. The engine is the system architecture that ensures 99.9% reliability. If you are an agency founder or SaaS operator, your value is not in "using AI"—it is in providing a reliable outcome that doesn't require manual supervision. Reliability is the only metric that matters at scale. Are you building for the demo or building for the deployment?

Huge shoutout to the Top 5 Members of AI Automation Society for November! These are the people leading the way, sharing knowledge, and helping spread the AIS culture: 1️⃣ @Muskan Ahlawat 2️⃣ @Kevin troy Lumandas 3️⃣ @Frank van Bokhorst 4️⃣ @Christian Rivadeneira 5️⃣ @Noel Doe Massive congrats to all of you for your consistency and energy this month. You help set the tone for the whole community. I also want to congratulate every single member of AIS! Because of your support, wins, questions, posts, and energy, AI Automation Society is officially the number two Skool community out of more than 200,000 communities, and number one in the tech category. That is insane. And it's because of YOU. Thank you for showing up, helping others, sharing resources, and building with us. This community keeps getting stronger every month, and that momentum is what makes AIS special. Let’s keep learning, growing, and pushing the future of AI automation together. 🚀 Cheers, Nate

From new client wins to digital twins and foundation-level breakthroughs - another week of real progress inside AIS+ Here are this week’s highlights inside AIS+ 👇 👉 Kevin Hardin built a job-sorting automation using Apify + n8n to instantly organize Upwork leads - a solid real-world workflow in action. 👉 @Elijah Williams restructured his entire agency model after completing the One-Person Agency course — turning insights into a roadmap. 👉 @Rakshith Gowda solved a Veo 3.1 API error after hours of debugging - a reminder that persistence pays off. 👉 @Simon Cousineau is now featured weekly on Canadian radio as their official AI expert - turning consistency into credibility. 👉 Malek Kilani built an AI-powered language coach in under an hour, shared the PRD, and inspired others to ship faster. 🎥 Super Win Spotlight of the Week: @Lutfiya Miller | From Toxicologist to AI Consultant Lutfiya went from learning AI workflows to landing two new clients at a scientific conference - proving what happens when confidence meets consistency. AIS+ helped her go from fear and uncertainty to building powerful automations and speaking at multiple events in 2025. 🎥 Watch her story 👇 Lutfiya’s journey is proof that when you replace fear with action, everything starts to click. ✨ Want to see more wins like these every week? Join the builders inside AI Automation Society Plus - where learning turns into confidence and results 🚀