https://drive.google.com/file/d/1U8MsBuIhegD36f3s6OuxCAdAhBEY_9Dr/view?usp=share_link

Database workload modernization fails when dependency graphs lack the semantic context to capture the ""why"" and ""how"" behind components and their connections. We present a framework based on LangGraph, transforming static mapping into a semantically enriched Knowledge Graph (KG) that serves as a control plane for autonomous migration agents. By extracting ontologies from code and telemetry, we build a ""Living Digital Twin"" enabling deep contextual reasoning.

The framework demonstrates 4 core capabilities:

Semantic Prioritization: Agents synthesize node semantics (entity classification, ownership) with edge semantics (coupling complexity, usage patterns) to design migration waves by calculating both intrinsic risk and extrinsic impact.
Blast Radius Analysis: Using multi-hop edge reasoning, agents trace dependencies across the ecosystem to predict how node-level schema changes propagate to upstream producers and downstream consumers.
Semantic Refactoring: Agents leverage ontological mapping to automate construct translation based on the inferred semantic role of a node within its broader graph neighborhood.
Workload Pruning: Agents intersect node metadata with edge telemetry to identify zombie, obsolete artifacts, reducing migration surface area.
An Agentic Quorum of judges audits each wave, creating a self-healing loop. This evolves KGs from passive maps to active reasoning infrastructure, combining formal ontologies with agentic frameworks for complex enterprise workflows.