Automation Workflow for Satellite Constellation Health and Debris Proximity Alerts

This workflow automates the critical operational bottleneck of manually cross-referencing hundreds of daily conjunction alerts with the unique health status of each satellite in a fleet. The business value comes from preventing unnecessary maneuvers on ailing assets that lack fuel or capability, while fast-tracking responses for high-value, healthy satellites. Savings are realized through optimized fuel expenditure, extended asset lifespan, and reduced analyst triage time, directly protecting constellation economics and mission assurance.

Implementation integrates a central orchestrator (e.g., LangGraph) with SSA APIs, the operator's satellite database, and flight dynamics software. The Priority Engine applies rules and ML models to score risk against battery levels, propellant, and attitude control status. Controls include configurable thresholds, mandatory review gates for degraded assets, and immutable audit logs for every alert routing decision. Observability is built via dashboards tracking alert volume, response latency, and fuel consumption per risk category.

This custom automation directly addresses the operational bottleneck of manually correlating hundreds of daily conjunction data messages (CDMs) with the unique health status of each satellite in a constellation. The business value comes from prioritizing alerts for satellites with degraded propulsion or power, preventing wasted fuel on low-risk maneuvers for healthy assets and focusing operator attention where collision risk intersects with operational vulnerability. Savings are realized through extended satellite lifespan, reduced analyst screening time, and lower risk of catastrophic, revenue-ending loss.

Implementation integrates with existing Mission Control Systems (MCS) like AGI's STK or proprietary platforms, and telemetry databases. The architecture uses specialized agents for data ingestion, health scoring, and risk fusion, orchestrated via LangGraph. Critical controls include human review gates for high-stakes alerts, confidence scoring on fused data, and immutable audit logs for every prioritization decision. Exception handling routes ambiguous cases for analyst review, ensuring the system augments rather than replaces operator judgment while compressing decision cycles from hours to minutes.

This workflow automates the critical operational bottleneck of manually cross-referencing hundreds of daily conjunction data messages (CDMs) with the unique health status of each satellite in a constellation. The savings come from reducing analyst screening time by over 80% and preventing costly collisions or mission degradation by prioritizing alerts for ailing assets with reduced maneuver capability. It directly improves fleet uptime and extends satellite operational lifespan through smarter, health-aware risk triage.

Implementation integrates via APIs with SSA providers like COMSPOC or LeoLabs, ingests telemetry from mission control systems (e.g., AGI STK, KubOS), and uses a rules-based scoring agent to assign risk tiers. A satellite with low battery or thruster anomalies is flagged for immediate operator review, while healthy assets receive consolidated reports. The architecture, built on frameworks like LangGraph for orchestration, includes mandatory human-in-the-loop approval gates for high-severity alerts and feeds into existing operational dashboards and ticketing systems like ServiceNow for auditability and action tracking.

This workflow automates the correlation of real-time debris conjunction data with individual satellite telemetry and subsystem health. It eliminates the manual, error-prone task of cross-referencing separate SSA and fleet management dashboards. The operational upside is direct: by routing alerts based on satellite capability—e.g., flagging conjunctions for satellites with degraded propulsion or power—operators protect high-value assets and make more efficient use of limited response bandwidth. Savings come from preventing catastrophic loss and optimizing analyst focus.

Implementation requires integrating APIs from SSA providers like COMSPOC or LeoLabs with internal telemetry systems (e.g., NASA's TETK or custom MQTT streams). A central orchestrator, built with frameworks like LangGraph, applies business rules to score and route each alert. Critical governance controls include mandatory human approval for any maneuver command, immutable logging of all automated decisions for regulators and insurers, and a phased rollout starting with a single orbital plane. This ensures system reliability is proven before fleet-wide deployment, managing operational risk.

The workflow's value hinges on ingesting high-fidelity, time-sensitive data from multiple sources. This includes real-time satellite telemetry (power, attitude, propulsion health) from your Mission Control System (MCS), conjunction data messages (CDMs) from services like SOCRATES or CARA, and ephemeris data from commercial SSA providers. The architecture must normalize this data, handle API failures gracefully, and apply initial validation to flag anomalous inputs before they corrupt the risk model. Data latency directly impacts warning time, making pipeline reliability a non-negotiable operational requirement.

Integration points define the system's operational control. The risk engine must write prioritized alerts and asset-specific summaries directly into the operator's dashboard (e.g., a custom Grafana or Kibana frontend) and case management system (like ServiceNow). For high-priority alerts on ailing satellites, the workflow should trigger automated notifications via PagerDuty or MS Teams to the on-call engineer. Crucially, the architecture needs a feedback loop where post-maneuver telemetry is ingested to validate the satellite's new state and update its risk profile, closing the operational loop and continuously refining fleet health awareness.