AI-Driven Workflow for Autonomous Satellite and Non-Terrestrial Network Routing

Manual monitoring and rerouting of satellite links (GEO/LEO) during weather events or congestion leads to minutes or hours of service degradation, directly impacting customer SLAs and revenue. An autonomous workflow uses agents to monitor signal quality (SNR, BER) and telemetry, predicting impairment and executing seamless failover to alternative satellites or ground stations in seconds, preserving uptime and preventing contractual penalties.

Network operations centers (NOCs) manually managing hundreds of satellite modem configurations, path calculations, and SD-WAN policies are a significant OPEX drain. A custom orchestration layer automates these repetitive tasks—continuously evaluating optimal paths, pushing configurations via APIs (e.g., to iDirect, ST Engineering modems), and integrating with terrestrial SD-WAN controllers—freeing engineers for strategic work and reducing headcount requirements for routine operations.

Satellite bandwidth is expensive, with costs varying by orbital slot, beam, and service provider. Static allocations lead to over-provisioning and waste. An AI-driven workflow employs cost-aware routing agents that continuously evaluate latency, congestion, and real-time pricing data from multiple satellite operators (e.g., Intelsat, SES, Starlink), dynamically shifting traffic to the most cost-effective path that meets performance SLAs, directly lowering monthly transit spend.

Provisioning new remote sites (oil rigs, maritime vessels, disaster recovery) requires manual coordination of satellite terminals, modem profiling, and integration with corporate WANs—a process taking weeks. A zero-touch provisioning workflow automates terminal boot-up, satellite acquisition, policy assignment, and secure tunneling backhaul via SD-WAN, enabling rapid scaling of global coverage and faster time-to-revenue for new enterprise services.

Unplanned satellite outages due to solar flares, space weather, or ground station failures create business continuity risks for critical operations (shipping, defense, remote mining). A predictive workflow ingests space weather feeds, ground station telemetry, and constellation health data to forecast potential disruptions, proactively rerouting mission-critical traffic hours in advance and triggering maintenance work orders, transforming risk management from reactive to predictive.

Static satellite service plans cannot monetize varying customer needs for latency or bandwidth. An autonomous routing workflow enables dynamic service tiering: real-time path selection can guarantee low-latency LEO paths for trading or telehealth, while shifting bulk data to cost-effective GEO links. This creates upsell opportunities and a competitive edge for operators, turning the network into a programmable, revenue-generating platform.

This agent ingests real-time telemetry from satellite modems and ground stations, including signal-to-noise ratio (SNR), bit error rate (BER), weather interference data, and physical layer congestion. It establishes a continuous baseline for each GEO/LEO link and flags degradations that threaten SLA compliance. Integration with external weather APIs and orbital ephemeris data allows it to predict rain fade and satellite handover events before they impact traffic.

A core decision agent that evaluates all viable paths—across different satellite constellations (e.g., Starlink LEO vs. GEO) and terrestrial backhauls—against a weighted policy of latency, cost-per-megabit, and reliability. It uses constraint-based routing algorithms to calculate the optimal failover or load-balancing action, dynamically adjusting BGP preferences or SD-WAN policies to steer traffic. This engine is where business logic (e.g., 'minimize transit cost for backup links') is translated into network instructions.

This system component executes the routing decisions by interfacing directly with SD-WAN controllers (Cisco, VMware, Fortinet) and ground station network equipment via APIs. It pushes new tunnel configurations, modifies priority groups, and updates next-hop definitions. For seamless failover, it must coordinate timing to prevent black-holing traffic and ensure session persistence for stateful applications. This layer is the critical bridge between the AI orchestration logic and the physical/virtual network fabric.

Not all decisions can be fully autonomous. This workflow component catches low-confidence recommendations, potential policy violations, or actions affecting mission-critical traffic and routes them to a human-in-the-loop console within the NOC. Engineers can approve, modify, or reject proposed changes, with the system logging all rationale for audit. This gate is essential for managing risk during the initial rollout and for handling novel failure scenarios the agents haven't been trained on.

A centralized system that aggregates telemetry from all agents and network points to provide a single pane of glass. It correlates agent decisions with resulting network KPIs (latency, throughput, cost), enabling operators to validate ROI and tune policy weights. It also generates automated reports on SLA compliance, cost savings, and incident root cause, turning orchestration activity into actionable business intelligence for network and finance teams.

The master orchestrator (built on frameworks like LangGraph or Temporal) sequences the workflow: monitor -> analyze -> decide -> execute -> verify. It enforces idempotency and includes a mandatory verification step post-action. If the new path fails validation, it automatically triggers a rollback to the last-known-good state. This controller is the operational backbone that ensures the entire workflow is reliable, traceable, and safe for production deployment across global sites.

The core agent must ingest real-time link quality data (C/N0, BER, weather forecasts) from GEO/LEO satellites and ground stations, then calculate optimal paths using constrained shortest-path algorithms. This logic must integrate seamlessly with terrestrial SD-WAN controllers (e.g., Cisco, VMware) via APIs to execute seamless failover, presenting a unified underlay to enterprise applications. The architecture requires a stateful orchestration layer (e.g., LangGraph) to manage the multi-domain decision sequence and maintain session persistence during handovers.

To avoid reactive scrambling, the workflow needs predictive agents that model satellite link degradation from weather (rain fade), solar activity, and ground station congestion. These models, trained on historical telemetry, trigger pre-emptive re-routing before service-impacting latency or packet loss occurs. Implementation involves time-series forecasting (Prophet, LSTM) integrated with the path calculation engine, requiring a continuous feedback loop from modem telemetry to validate forecast accuracy and retrain models.

Fully autonomous routing in critical connectivity scenarios is untenable without guardrails. The architecture must include approval gates for major policy shifts (e.g., shifting primary traffic to a higher-cost LEO constellation) and real-time monitoring dashboards for NOC oversight. Exception handling workflows must route ambiguous or high-risk decisions to human operators, with full audit trails of all agent decisions, link states, and triggered actions for post-incient review and compliance.

The workflow's effectiveness depends on low-latency, high-fidelity data from specialized satellite modems (e.g., Comtech, Gilat) and network terminals. Building a robust telemetry pipeline involves developing or leveraging vendor APIs to stream signal metrics, then normalizing this data into a unified schema for the AI agents. This is often the most time-intensive integration phase, requiring custom adapters and validation to ensure data quality before it drives autonomous decisions.

The primary ROI drivers are the reduction in NOC labor for manual link monitoring and re-routing, and the avoidance of revenue-impacting outages for remote operations (mining, shipping, energy). By automating congestion management and failover, the system directly converts to lower operational overhead and protects SLA-based revenue streams. A pilot typically quantifies reduced mean time to repair (MTTR) for link failures and increased effective throughput during peak congestion periods.

Deployment must be phased, starting with monitoring and recommendation-only mode in a non-critical network segment. The second phase introduces automated failover for pre-defined, low-risk scenarios. Full autonomous operation is the final phase, contingent on validation against a digital twin or shadow mode that compares agent decisions to historical optimal paths. This cautious approach mitigates risk and builds operational trust, while each phase delivers incremental value.