Maneuver Trade-off Analysis Automation: Risk vs. Fuel vs. Mission Impact

Every conjunction alert forces satellite operators into a manual, high-pressure trade-off analysis using spreadsheets and disparate tools. This custom automation workflow replaces that bottleneck with an AI agent that ingests conjunction data, satellite state, and mission schedules to model multiple maneuver options. It quantifies the delta-V cost, collision probability reduction, and revenue impact of lost imaging or communication passes. The output is a ranked set of executable recommendations with clear financial and operational implications, compressing a multi-hour analysis into minutes and ensuring decisions preserve both asset safety and business value.

Implementation integrates with systems like AGI's STK or open-source propagators for high-fidelity trajectory modeling, and connects to mission planning software and ERP for cost data. The architecture includes approval gates for high-risk scenarios, continuous monitoring of post-maneuver telemetry for validation, and an immutable audit trail logging all inputs, logic, and outcomes for regulatory and insurance review. This turns a reactive, artisanal process into a controlled, scalable operating procedure that directly extends satellite lifespan and protects revenue.

This workflow automates the high-stakes, repetitive analysis where operators manually weigh fuel expenditure against collision risk and data loss. By quantifying trade-offs in real-time, it directly reduces decision latency from hours to minutes, prevents costly over-maneuvering that shortens satellite life, and preserves mission revenue by minimizing unnecessary payload downtime. The system ingests live conjunction data messages (CDMs), telemetry, and mission schedules to model multiple maneuver options under operational constraints.

Implementation integrates a LangGraph or custom orchestrator to manage specialized agents for risk, fuel, and mission analysis, feeding a central decision engine. The architecture connects to high-fidelity propagators like Orekit or AGI's STK, satellite command and control systems, and financial models. Critical controls include human approval gates for all non-emergency maneuvers, confidence scoring for each recommendation, and immutable audit logs capturing all data inputs and agent reasoning for regulatory and insurance review.

Phase 1 establishes the core decision-support engine, ingesting conjunction data messages (CDMs) and satellite telemetry via secure APIs from systems like SOCRATES or proprietary SSA feeds. An orchestrator, built on LangGraph, triggers a multi-criteria optimization agent that models 5-10 maneuver options, quantifying collision probability delta, propellant cost, and data/productivity loss. This initial build replaces manual spreadsheet analysis, delivering a prioritized recommendation dashboard to operators within minutes, not hours, compressing the initial assessment loop.

Phases 2 and 3 introduce advanced simulation and fleet-wide coordination. The system integrates high-fidelity propagators (e.g., Orekit, GMAT) for Monte Carlo risk analysis and deploys a fleet-level agent to optimize total propellant burn across coordinated satellites, preventing operational fratricide. Final phases harden the workflow with automated audit trails for regulators, explainability layers for insurer reporting, and secure cross-operator coordination protocols, transforming a tactical tool into a strategic, governed operating system for orbital safety.

This custom workflow automates the high-stakes, manual spreadsheet analysis satellite operators perform when a conjunction alert arrives. It replaces hours of engineering labor with seconds of multi-criteria optimization, directly impacting operational economics by extending asset lifespan through fuel-aware decisions and protecting revenue by minimizing mission disruption. The system ingests conjunction data messages (CDMs), precise orbit determination, satellite health telemetry, and the scheduled mission timeline to model a set of candidate maneuvers, each with quantified trade-offs.

Implementation integrates with mission control systems (MCS) like COSMOS or commercial platforms, and high-fidelity propagators (e.g., Orekit, FreeFlyer). The orchestrator, built on frameworks like LangGraph, manages the data flow between specialized agents for propagation, fuel modeling, and mission impact scoring. Critical controls include pre-defined fuel thresholds, mandatory human approval for high-cost options, and an immutable audit trail logging all inputs, agent reasoning, and the final decision rationale for regulators and insurers.

This custom workflow automates the high-stakes, manual spreadsheet analysis satellite operators perform when a conjunction alert arrives. It replaces hours of fragmented calculation with a unified multi-criteria optimization engine that models dozens of potential burn solutions in parallel. The business value is direct: it prevents costly over-maneuvering that wastes precious propellant and shortens satellite life, while also quantifying the revenue impact of lost imaging or communication passes, enabling operators to choose the most economically optimal avoidance path.

Implementation integrates high-fidelity propagators (like Orekit or GMAT) with a LangGraph-based orchestrator that manages the optimization loop. The agent ingests live telemetry for fuel state and scheduled mission events from the satellite's mission planning system. Each option's output includes a quantified cost-benefit matrix, projecting remaining propellant life and financial impact. Critical controls include a mandatory human review gate for all non-emergency maneuvers and a full audit trail capturing all input data, model weights, and the final decision rationale for regulator and insurer reporting.