Hi everyone,
I’m an AI automation consultant building production n8n/AI systems — not just chatbots, but agents wired into real business data and processes.
Most of what’s marketed as “AI agents” is a single LLM call with no real safety layer around it. What I build is different: every agent sits behind authenticated access control, single-purpose tool scoping, and isolated data access — so the agent can act on real systems without becoming a direct line into your database.
Systems I’ve built on this architecture:
- AI customer support agent (WhatsApp, Telegram, web) — text and voice, live database lookups for orders/billing/stock, human escalation via Slack with full context handoff
- Appointment scheduling/modification/cancellation via Cal.com
- MCP-based tool integrations so agents can query external systems directly, not just canned API calls
- Business intelligence agent connected to Supabase — ask questions about your own data in plain language or voice
- Automated transactional comms via Resend
- Analytics dashboard tied to the live support database
I work directly with businesses, and I also partner with agencies who sell automation but want a technical partner for the backend engineering — MCP connectivity, database integration, escalation logic — delivered white-label under your brand.
Architecture and product breakdown here: https://www.jm20aisolutions.com/
Happy to talk through a specific project or a partnership setup.
Very interesting! I agree totally on the point that most AI agents are single LLM calls with no real safety layer around it… My specialization is more on the safety layer, i think we could do something together, if youre interested take a look on guardian-safety-gate.com or just DM me.
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This looks awesome! You’ve nailed a massive pain point here—anyone building with AI right now is terrified of an agent accidentally going rogue with payments or data, so a human-in-the-loop policy layer is genius. The copy feels super crisp, and the “Policy, not just permissions” angle makes total sense. I would absolutely love to collaborate with you on this project.
Let me know what you think—would you want to team up on refining the onboarding flow, tightening up the technical copy, or brainstorming the integration hooks?
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Thanks for the kind words! Your architecture is solid - single-purpose tool scoping and isolated data access is exactly how production agents should be built. Most people skip that entirely.
The one thing I’d add: even with scoped tools and authenticated access, you still need a decision layer before the agent acts. “This agent has access to the CRM” is access control. “This agent can modify 200 customer records without anyone reviewing it” is a governance gap.
That’s the piece I’ve been building - a policy gate that sits between the agent’s intent and the actual tool call. Payload-aware rules (block actions above a threshold, require approval for sensitive operations), human approval via a web dashboard, and a tamper-evident audit log with SHA-256 payload hashing so you can prove what was approved is what actually ran.
For your Slack escalation flow specifically - that’s manual right now. Someone has to watch Slack, read the context, and type “approved.” A structured approval gate would give you the same human checkpoint but with a proper audit trail, approval deadlines, and policy rules that auto-handle the low-risk stuff so only genuinely dangerous actions need human attention.
Would be curious - how do you decide today which actions need human review vs. just letting the agent run? Is that hardcoded per workflow?