From the standpoint of enterprise and service provider production networks, BGP path manipulation is perhaps among the most important of functions due to its intimate control over ingress, egress, and flow through their network infrastructure. BGP attributes that influence routing decision for traffic engineering, redundancy, load balancing based on different makeup of a path & latency, disaster recovery using Local Preference, AS Path Prepending, MED which stands for Multi-Exit Discriminator/Weight & Community values. In large customer-production environments, wrong BGP path selection can create routing loops, asymmetric routing, congestion, and packet losses impacting service levels leading to SLA violations and even the complete outage of services affecting customers and business-critical applications. As a result, network engineers must consider carefully any change of policy to adopt it anywhere. Here, it is where —Agentic AI enables— the biggest speedup: it constantly analyses real-time telemetry, BGP updates, historical incidents & network topology data to automatically/syn-autonomously make intelligent routing decisions. Powered by Digital Twin Simulation and AI Insights, the agent predicts the effects of BGP policy changes before operationalizing them, then monitors anomalous route behavior by detecting unstable peers, and finally proposes routing paths with maximum stability and minimum risk. This allows dynamic adjusting of Local Preference, intelligent traffic rerouting after congestion detection, rampant route leak prevention and decreased blast radius in case of failure-all while continuing to meet SLA. With observability, simulation and autonomous decision-making, Agentic AI changes a legacy reactive approach to BGP operations into one that is proactive, self-healing with extremely resilient network management.
Scenario: Brayan is working as a Network Optimization Engineer at Wipro Telecom in an enterprise network environment. Based on a customer requirement, BGP path manipulation needs to be implemented to optimize traffic flow across the network. Currently, one network path is experiencing continuously increasing traffic utilization, while another segment is impacted due to an OFC (Optical Fiber Cable) cut, resulting in congestion, instability, and potential SLA degradation. To provide a better resolution, the enterprise network must intelligently reroute traffic by modifying BGP attributes such as Local Preference, AS Path Prepending, MED, or Community values to balance traffic and ensure high availability. Traditionally, this process requires manual analysis and configuration by network engineers, which can increase response time during critical incidents. However, with Agentic AI, the system can automatically analyse real-time telemetry, detect congestion and link failures, simulate the impact of routing changes using a Digital Twin topology, and autonomously perform optimized BGP path manipulation. This enables faster convergence, reduced downtime, improved traffic engineering, SLA protection, and intelligent self-healing operations. Let us understand this concept using a simple network topology example.
Phase 1 — The Telemetry & Observability Foundation
It lays the groundwork for AI-enabled network operations to monitor in real time and across data silos. Simulates gNMI streaming telemetry from BGP routers as configured with OpenConfig YANG models to export metrics about network statistics, routing updates, CPU usage and protocol health. A BMP collector listens for BGP route advertisements, withdrawals and peer state changes. We stream all telemetry data through a Kafka-style event bus and store it in an InfluxDB-style time-series database for later use to analyze what has occurred and tips on troubleshooting. The feature extraction modules convert this raw telemetry into low-dimensional AI-ready datasets that serve as input to a wide range of ML algorithms and models like anomaly detection, predictive analytics, root-cause analysis, autonomous remediation etc. And in prod, all of the agents are replaced by actual technologies such as pygnmi / gnmi-py, GoBMP, Confluent Kafka and InfluxDB clients to provide a scalable and reliable network observability.