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My end-to-end approach to shipping AI agents: voice or chat interface, an agent layer with tool-calling and guardrails, deterministic logic for anything that must be auditable, and persistent memory in PostgreSQL + pgvector.
Built on Python 3.11, FastAPI, and OpenAI Agents SDK. Anthropic MCP certified.
I also lead applied AI instruction for a government-recognized national program, teaching this same stack to working engineers.
The network for creativity
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Creatives on Contra have earned over $150M and we are just getting started
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A production-ready LangGraph agent that discovers, audits, scores, enriches, and qualifies B2B prospects automatically.
I recently built this workflow in n8n to automate the first stage of handling customer support requests.
The idea was to take an incoming ticket and let the system handle the repetitive work:
→ Receive the customer request through a webhook
→ Process and structure the incoming data
→ Use an AI Agent to understand the ticket
→ Classify it as Genuine, Spam, or Unclear
→ Route it based on the classification
→ Trigger the appropriate email action
→ Log the interaction in Google Sheets
I also added an Unclear path so that tickets the AI isn’t confident about don’t get blindly processed.
The goal isn’t to remove humans from customer support.
It’s to reduce the repetitive sorting and routing work, so the team can spend more time on the requests that actually need their attention.
Would you automate customer support differently? I’d love to hear how you’d approach it.
The Unclear lane is exactly the right guardrail. I’d add two production checks: redact PII before sending ticket text to the model, and measure false-positive/false-negative rates by category rather than a single accuracy score. Logging resolutions and human overrides back into...
An AI-powered digital technology platform designed to turn real business problems into intelligent products and systems.
Kibani Digital combines AI, data, automation and software development into connected digital solutions for businesses.
The platform concept brings together AI agents, knowledge, CRM, analytics, automation, integrations, customers, projects and business tools.
Capabilities:
AI solutions · automation · data analytics · digital transformation · business intelligence · APIs · integrations · custom software.
