Building Self-Healing AI Agents with Ontologies and MCP | Real Production Architecture
Автор: The Civic Stack
Загружено: 2026-01-17
Просмотров: 12
Most AI agents work great in demos — and fail silently in production.
In this video, I break down a real-world production problem where a simple database column rename (customer_id → client_reference_id) caused an AI agent outage — and how we solved it using ontologies, MCP (Model Context Protocol), and a self-healing architecture.
You’ll learn:
Why hardcoded schemas make AI agents brittle
How ontologies act as a semantic translation layer
How MCP turns your ontology into reusable agent tools
How AI agents can automatically adapt when schemas change
Why this approach scales to multi-agent systems in enterprises
This pattern is already reshaping how we build reliable, production-grade AI systems — especially in GovTech, FinTech, and large enterprises.
🔗 Medium article:
👉https://medium.com/@cloudpankaj/from-...
💻 GitHub repository:
👉 https://github.com/cloudbadal007/onto...
📺 More content on AI, GovTech & production systems:
👉 Subscribe to The Civic Stack https://badalaiworld.substack.com/
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