TopoEdge: Topology-Grounded Agentic Framework for Edge Networking Code Generation and Repair
This addresses the problem of reliable and efficient network configuration management for edge networking operators, though it is incremental as it builds on existing retrieval-augmented generation and agentic methods.
The paper tackles the brittleness of software-defined networking (SDN) configurations under topology changes by introducing TopoEdge, a framework that uses a graph neural network and multi-agent system to generate and repair configurations, achieving a 92% success rate in configuration generation and repair across diverse topologies.
TopoEdge is a topology-grounded, edge-deployable framework for end-to-end software-defined networking (SDN) configuration generation and repair, motivated by the brittleness of configuration artefacts under topology variation and by strict operational constraints on latency, privacy, and on-site execution. TopoEdge represents each target topology as a router-level graph and embeds it using a contrastively trained graph neural network (GNN), enabling nearest-neighbour retrieval of a verified reference configuration paired with an executable Python driver (a Topotest/pytest test script that orchestrates the emulated network and checks protocol assertions). The target topology, retrieved reference topology, and reference driver are assembled into a topology-grounded retrieval-augmented generation context (TopoRAG), which grounds a distributed, execution-centric generate--verify--repair loop coordinated by a central controller and realised by three role-specialised agents: (i) a Planning agent that produces a topology-consistent configuration plan and a per-device skeleton; (ii) a Generation agent that materialises executable configuration artefacts, including device configurations and the driver; and (iii) a Verification agent that runs the FRRouting Topotest/pytest harness, compresses failures into a compact trace, and emits localised patch directives for iterative repair.