Multi-Agent Based Automation of Wholesale Carrier Peering and Transit Optimization

This workflow automates the manual, reactive process of traffic engineering between wholesale carriers and their peers. Agents ingest real-time NetFlow, BGP feeds, and commercial pricing data to model cost-performance trade-offs. By continuously adjusting BGP local preference and communities, the system shifts traffic to optimal paths, eliminating overpayment for premium transit during off-peak hours and maximizing free peering where performance allows. The operational upside is direct margin improvement and reduced network operations center (NOC) toil.

Implementation integrates with network management systems (NMS) like Cisco NSO or custom controllers via RESTCONF/NETCONF to execute BGP policy changes. The architecture requires robust simulation—using a digital twin of the routing domain—and approval gates for high-risk adjustments to prevent route leaks. Observability is built in, with agents logging rationale and outcomes for audit. Rollout is phased, starting with low-risk traffic classes, and governed by strict change control integrated with ServiceNow.

This workflow automates the manual, expert-intensive process of analyzing traffic flows to peers and transit providers, then adjusting BGP communities and local preference. The operational upside comes from reducing transit costs by shifting traffic to lower-cost paths and improving performance for high-value wholesale services, directly impacting gross margin. Implementation requires high-scale flow ingestion from NetFlow/IPFIX collectors, integration with commercial pricing data (e.g., Telegeography), and a decision engine that respects business rules and contractual commitments.

The architecture is built on an orchestration layer, likely using LangGraph or a custom state machine, to sequence agent tasks, manage state, and enforce guardrails. Critical controls include a human-in-the-loop approval gate for major policy shifts, simulation against a network digital twin before deployment, and comprehensive observability via integration with tools like ServiceNow and Splunk. This ensures the system operates safely at carrier scale, providing auditable, incremental optimizations that directly reduce monthly transit spend.

This workflow automates the complex, manual process of traffic engineering for wholesale services. Agents continuously analyze NetFlow and BGP data against commercial feeds from providers like PeeringDB and LINX, identifying opportunities to shift traffic to lower-cost or higher-performance peers. The operational upside comes from direct transit cost reduction, improved service quality for enterprise customers, and freeing senior network engineers from routine BGP tuning. Implementation requires high-scale telemetry ingestion, a decision engine with cost/performance models, and safe, incremental BGP session manipulation via platforms like Nautobot or Batfish.

Phased delivery is critical for managing risk. Phase 1 implements monitoring, analytics, and a read-only digital twin for simulation. Phase 2 adds approval-gated, manual execution of agent recommendations. Phase 3 enables fully autonomous optimization for pre-approved change classes, with mandatory human-in-the-loop escalation for anomalies or high-risk adjustments. The architecture integrates with existing OSS/BSS stacks like Amdocs or Netcracker for commercial data and ServiceNow for change management, ensuring governance and auditability throughout the optimization lifecycle.

A phased rollout begins with a read-only analysis agent, validating its traffic and cost recommendations against historical engineering decisions without making live changes. This sandboxed phase builds confidence in the agent's logic and data sources, such as NetFlow, PeeringDB, and commercial transit pricing feeds. Governance is enforced through a central orchestrator that logs all proposed BGP community or local-preference adjustments for audit, requiring explicit approval gates before any session manipulation via platforms like Nornir or Batfish.

Post-validation, the system graduates to a controlled-write phase, where low-risk adjustments—like prepending to a secondary transit provider—are executed automatically within predefined guardrails. A real-time validation agent monitors key latency and packet-loss metrics post-change, triggering an automatic rollback if performance degrades. The final phase introduces full autonomous optimization for pre-approved policy domains, with all actions recorded in the CMDB and integrated into ITSM for full auditability. This structured approach de-risks the transition from manual traffic engineering to a continuous, agent-driven operating model.