Data platforms treat infrastructure and derived decisions as separate worlds. A unified dependency graph joins them so agentic systems can trace outputs back to raw inputs without losing the thread across transformations. By Animesh Kumar.
The article outlines these ideas:
- Infrastructure and derived outputs are structurally distinct and must be treated as such.
- Agentic AI compounds hidden failure modes by chaining outputs into inputs across systems.
- Existing lineage and governance tools are disconnected point solutions; they need a unified dependency graph.
- Every AI output needs an automatic, machine-readable pointer to exact inputs and model version.
- An untraceable output is acted on as fully real despite being structurally unverified.
- Adopt a unified graph if outputs drive automated actions and silent drift is unacceptable; defer if lineage is a single-person memory; investigate by tracing one model to raw inputs first.
A platform’s task extends beyond storing data or even enabling AI: it is to hold the full chain, from infrastructure to insight, inside one structure that can be traversed and audited, so nothing an AI system does rests on a foundation nobody can see. Every AI output should carry an automatic, machine-readable pointer to its exact inputs and model version. An output that cannot be traced back to what it depends on has no settled place in the system that produced it. Interesting read!
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