Fleet Operations: Managing Hundreds of Satellites' Debris Risk Collectively

The operational bottleneck is the manual, satellite-by-satellite review of conjunction data messages (CDMs), which scales linearly with fleet size. A custom automation workflow replaces this with a hierarchical agent system: perimeter agents screen CDMs for each satellite, applying ML filters to eliminate false positives. Valid risks are escalated to a central orchestrator agent that maintains a global risk picture, identifying cross-fleet conflicts where one satellite's evasive maneuver could create new conjunctions with another asset. This architecture prevents operational fratricide—a critical failure mode for dense constellations—and compresses fleet-wide risk assessment from days to minutes.

Implementation integrates with systems like AGI's STK or custom propagators for high-fidelity simulation, and platforms like Kubos or AWS Ground Station for command upload. The orchestrator uses optimization libraries (e.g., Gurobi, CP-SAT) to solve for fuel-minimized, conflict-free trajectories. Controls are mandatory: all coordinated plans require human review via a dashboard like COSMOS before command generation. Observability is provided through audit trails for each automated decision and continuous post-maneuver validation against updated debris catalogs. The ROI comes from eliminating manual coordination labor, reducing total fleet propellant burn by 15-30% through optimized planning, and mitigating the catastrophic financial and regulatory risk of an in-orbit collision within your own constellation.

For mega-constellation operators, manually managing conjunction risk across hundreds of assets is operationally infeasible and economically unsustainable. A hierarchical multi-agent system automates this by distributing risk assessment to per-satellite 'Fleet Agent' nodes, which perform initial screening and health-aware prioritization. These agents report to a central 'Orchestrator Agent' that resolves conflicts, optimizes for total fleet fuel burn, and generates a synchronized, conflict-free maneuver plan. This architecture replaces slow, sequential human analysis with parallelized, automated decision-making, compressing response timelines from days to hours and directly protecting asset lifespan and service revenue.

Implementation requires integrating this agentic layer with existing flight dynamics software (e.g., AGI's STK, FreeFlyer), mission control systems, and SSA APIs. The Orchestrator Agent, built on frameworks like LangGraph, manages state and communication between specialized sub-agents for data fusion, optimization, and compliance reporting. Critical controls include pre-maneuver simulation against the full catalog to prevent chain-reaction conjunctions, configurable approval gates for different risk thresholds, and immutable audit trails for every automated decision to satisfy regulatory and insurance requirements.

Phase 1 establishes the core data ingestion and risk-screening layer. We integrate commercial SSA feeds (e.g., LeoLabs, ExoAnalytic) and internal telemetry into a unified data lake, then deploy initial screening agents that filter conjunction data messages (CDMs) using rule-based and ML models to reduce false positives by 70-80%. This immediately cuts daily analyst screening time, providing a clear ROI while building the clean data foundation required for advanced orchestration. The architecture uses event-driven microservices on cloud infrastructure for scalability.

Phase 2 introduces the hierarchical orchestration layer, built with frameworks like LangGraph or custom agents. A central 'Fleet Manager' agent receives prioritized alerts, delegates assessment to 'Satellite-Specific' agents that evaluate local health and constraints, and runs a conflict-resolution solver to prevent chain-reaction maneuvers. This phase delivers the core capability for collective risk management. Phase 3 integrates the fuel-aware maneuver planner and automates cross-operator coordination and regulatory reporting, completing the transition from a monitoring system to a proactive, economically optimized fleet operations command center.

Rollout begins with a shadow-mode pilot on a single orbital plane, where the autonomous system ingests live SSA data and generates maneuver recommendations without command authority. This phase validates data pipelines, agent logic, and the integration with existing flight dynamics systems like AGI's STK or internally built propagators. Concurrently, governance frameworks are established, defining clear escalation thresholds, mandatory human-in-the-loop approvals for any delta-V burn, and immutable audit logging for every risk assessment and proposed action.

Following a successful pilot, deployment expands plane-by-plane, with each phase introducing greater autonomy. The final control layer is a rules-based 'circuit breaker' that can override any agent recommendation if predefined safety bounds—such as minimum fuel reserves, proximity to critical mission operations, or loss of ground communication—are breached. This governance model, combined with phased validation, allows operators to capture the massive efficiency gains of fleet-wide orchestration—reducing analyst screening labor by over 90% and optimizing total fuel burn—while systematically mitigating the operational and regulatory risks of autonomous space traffic management.