Recursive Ontologies: The Self-Healing Backend
By Imran Siddique
We have built the constraints (Part 1), the firewall (Part 2), and the headless protocols (Part 3). But static systems die. In a world where data changes every second, how do we keep our “Semantic Firewall” from becoming a legacy blocker?
The answer is Recursive Ontologies. The system must update itself.
The Feedback Loop: Agents as Telemetry
In previous articles, I explored how agents need feedback to know if they are doing well. We need to apply that same logic to the architecture itself.
When an agent fails to find an answer, that is not an error — it is a signal.
- Signal: “I could not resolve the dependency for Project X.”
- Action: This signal is captured by a background “Analyst System.” It doesn’t force the live agent to hallucinate an answer. Instead, it flags the Knowledge Graph as “Stale” or “Incomplete” in that specific sector.
The architecture analyzes these signals to trigger evolution. We don’t manually update the database; the system “heals” its own knowledge gaps based on the friction points of the agents living inside it.
Ephemeral Graphs: The “Just-in-Time” Knowledge Base
One of the biggest mistakes we make is treating Knowledge Graphs like monolithic databases that live forever. They shouldn’t.
True scale comes from making knowledge Ephemeral and Event-Driven.
- The Org Graph: Should be recreated only when an HR event triggers a change.
- The Product Graph: Should be rebuilt the moment a documentation PR is merged.
- The Context Graph: Should perhaps exist only for the duration of a project.
These graphs should be small, personalized, and highly disposable. By making them temporary, we subtract the complexity of maintaining “one big truth” and replace it with “current, specific truths.”
Human Wisdom: Statistical Supervision
If the system evolves itself, where does the human fit?
We cannot have humans reviewing every node update — that defeats the purpose of AI. But we cannot have AI rewriting its own logic without oversight — that creates drift.
The solution is Sampling for Wisdom.
We need a “Human in the Loop” strategy based on statistical sampling. Humans review a random 5% of the graph updates or only the updates with high “confidence variance.” We provide the Wisdom — the strategic intent — while the AI handles the Volume of execution. We stop being gatekeepers and start being auditors.
The Death of the “Software Engineer”
This brings us to a hard reality: The role of the “Software Engineer” as we knew it is gone.
Entry-level coding is dead. If your value proposition is converting requirements into syntax, AI has already replaced you.
- The New Junior: Is now a Systems Designer. They must spec the system and review the AI’s code.
- The New Senior: Is now an Architect. They focus on integration and topology.
- The New Architect: This is the role that is currently undefined.
I call this role The Cognitive Systems Architect.
This person isn’t just a backend engineer, and they aren’t just an ML researcher. They are the bridge. They possess a deep understanding of scalable, distributed systems (the “Ground Reality”) but also intuitively understand the probabilistic nature of AI (the “Limitations”).
Their job isn’t to write code. Their job is to design the constraints , the telemetry , and the feedback loops that allow humans and AI to co-develop reliable systems at scale.
The tools of yesterday won’t build the architectures of tomorrow. It’s time to stop coding and start orchestrating.
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