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Owned by Zillion

168 contributions to AI AUTOMATION INSIDERS
Ai voice agents & dm setting
anyone pretty good at delivering this? i need it
0 likes • 5d
Can you give more details? What apps are you looking set this up for or just phone and sms? Inbound or Outbound? What industry/niche?
🤖🏢 Building a Real AI Employee System with n8n — How Would You Architect This for Production? ⚙️🧠🚀
Hello. 😊 ⠀ First of all, thank you very much for creating this community where people can learn about AI automation and for sharing so much valuable information and knowledge. 🙏✨ ⠀ I am currently building a small company where AI is intended to work not merely as a chatbot, but more like an actual “employee.” 🤖🏢 ⠀ After reading the posts and ideas about AI automation shared in this community, I wanted to ask for advice from people with real experience, so I decided to post these questions. 😊 ⠀ ━━━━━━━━━━━━━━━━━━━━ 🏢 Our Current System ━━━━━━━━━━━━━━━━━━━━ ⠀ Our current setup uses n8n as the central workflow and operational orchestration system, OpenAI API for AI processing, Google Drive for storing instructions, knowledge, and deliverables, ChatGPT for planning and review, and Antigravity for browser and GUI operations that cannot be handled reliably through APIs alone. ⚙️🧠📁🌐 ⠀ We also own around 6,500 n8n workflow templates, so instead of building everything from scratch, we are trying to reuse existing templates wherever possible while developing the system. 🧩✨ ⠀ At the moment, some parts of the process are already working in testing. ⠀ However, our most important challenge now is to build a system in which one AI employee can reliably complete a real business task from beginning to end. 🔴🤖 ⠀ ━━━━━━━━━━━━━━━━━━━━ 🔄 The Workflow We Are Trying to Build ━━━━━━━━━━━━━━━━━━━━ ⠀ The ideal workflow is: ⠀ Automatically receive a job → read the latest company knowledge and instructions → perform the actual work → run a quality check → automatically correct the output if it fails the quality check → save the completed deliverable to Google Drive → mark the job as complete → automatically take the next job ⠀ That is the workflow we are aiming for. 🔄📁✅ ⠀ I would like human involvement to happen only when a task genuinely requires a human. ⠀ For example, login, approval, payment, identity verification, CAPTCHA, or other actions that may be difficult to reverse. 👤🔐💳⚠️
0 likes • 5d
Scaling up you'll probably want postgres for run queue and status, vector knowledge bases and client segregation as querying a vector db is more efficient than shoving all the knowledge into the LLM. Google drive is fine though to start for simplicity. For duplicate execution can set IDs and status for each job attached to the job queue and status updates at specific checkpoints. Minimize human approval gates in the middle of workflows. If there's a human action, make that the trigger for the next workflow. Generally smaller workflows are better and easier to find issues. Also easier for resuming workflows. API cost reduction is iterative, checking which cheaper models will suffice, storing data as opposed to rescraping etc. Service outages suck. Get used to it. You want as little as possible left to AI decisions. There should be no need for a dispatcher if you have a job queue setup. Do as much as you can programmatically. Setup evals for your AI steps. You can even use multiple models to eval for quality control and iterative improvement. What you're measuring as "good enough" is defined by you. That definition needs to be very clear and articulatable. Not sure n8n would be best for logins and payments with human in the loop necessary as there are timeouts and restrictions and you would need someone always available for this. n8n is more for automating production workflows than creating AI do it all employees. If you want an employee/s that you will need to interact with like a real employee get an agent like Hermes.
Hallucinations Software?
I created a skill for Claude Code to do a complex data analysis and create a 1500 word report from it. I prompted it to give a final review like an Editor in Chief and it looks amazing... but how would I know if there were hallucinations other than having a human review it? My human costs thousands for this work. I was thinking maybe plug the raw data into Codex and see if the findings come back the same? Any other tricks or software to verify the work?
1 like • 5d
@Rick Pendrick Are you more concerned about incorrect analysis or using hallucinated data? For data you can get the report to include sources for each claim and use another session or Codex be a claim/source checker. For the analysis it's more difficult to judge but you have the right idea if you want to minimize hallucinations, basically setup an LLM-as-a-Judge eval system. You can use Claude to call itself and Codex to source check and analysis evaluators and give you scores for groundedness and relevance. There are also dedicated models that do hallucination detection like Lynx.
0 likes • 11d
Not sure about promo code but you can grab Hermes agent first for free: https://hermes-agent.nousresearch.com/
10 Small Skills or 1 Big Skill?
Hey guys. How do you know whether to make 10 smaller skills or 1 big skill that emcompasses everything? I basically spent a week putting my entire process into 1 big skills just using claude, I have not even begun N8N yet, but it seems Claude can do automations as is..... but when is the time to cap how much is going into 1 skill? thanks!
2 likes • 11d
Use skills specific to the task ie. 1 task, 1 skill. Especially if it's just 10 skills. You can ask Claude to help you split up the skill into workflows if you use certain parts of the skill together. Claude can do automations, but is not as reliably as n8n. Wouldn't use claude for production automations, but if it's just a scheduled task you need to do once a day or once a week, Claude should be sufficient.
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Zillion Wong
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@zillion-wong-8796
Executive and Business Coach for Startup Entrepreneurs - AI Automations Architect

Active 22h ago
Joined Aug 19, 2025
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