Cost-Benefit Analysis Automation for Performing vs. Skipping a Maneuver

Every evasive maneuver consumes irreplaceable propellant and disrupts revenue-generating operations. The decision to burn or not is a complex trade-off between immediate operational cost and the probabilistic financial risk of collision, including satellite replacement, liability, and insurance implications. Manual analysis is slow, inconsistent, and cannot scale with the growing volume of conjunction alerts. Automating this cost-benefit analysis converts a reactive, gut-feel decision into a repeatable, auditable financial operation that protects asset lifespan and mission margin.

Implementation requires integrating the orchestrator with systems like AGI's STK for high-fidelity propagation, the satellite's telemetry database for fuel state, and financial modeling APIs. The workflow must include explicit approval gates for high-risk scenarios and produce a defensible audit trail linking the AI's probabilistic risk assessment, the cost model outputs, and the final operator decision. This turns a critical economic choice into a governed, scalable operational process.

This workflow automates a critical operational bottleneck: the manual, time-intensive analysis required to decide if a conjunction risk justifies the cost of an evasive burn. It directly impacts satellite lifespan and mission profitability by preventing unnecessary fuel expenditure while avoiding catastrophic loss. The system ingests real-time conjunction data, satellite health telemetry, proprietary cost models, and insurance parameters to calculate a net-present-value recommendation, compressing a multi-hour analyst task into seconds and providing a defensible, auditable rationale for each decision.

Implementation integrates with systems like AGI's STK or custom propagators for high-fidelity trajectory modeling, and ERP or financial databases for cost inputs. The architecture enforces governance through mandatory human approval for high-cost or high-probability scenarios before any command upload. Observability is built in via immutable audit logs capturing all data inputs, agent reasoning, and the final NPV calculation, which is critical for insurer reporting and regulatory compliance. This turns a reactive, gut-feel decision into a repeatable, economically optimized operating procedure.

This workflow automates a critical operational bottleneck: the manual, time-sensitive analysis required to decide if a conjunction warrants a costly maneuver. It ingests Space Situational Awareness (SSA) data, satellite telemetry, and business parameters (replacement cost, insurance terms) to model the Expected Monetary Value (EMV) of action vs. inaction. By replacing spreadsheet-based guesswork with a real-time, rules-driven agent, operators achieve consistent, auditable decisions that protect high-value assets while optimizing fuel expenditure and mission uptime, directly impacting satellite net present value and operational margins.

Implementation integrates with systems like AGI's STK or custom propagators for high-fidelity trajectory modeling, and ERP/financial systems for cost data. The orchestrator, built on frameworks like LangGraph, manages the data flow between risk probability engines, fuel models, and business rule agents. Critical controls include pre-defined confidence thresholds for automated 'skip' decisions, mandatory human review for high-value assets or edge cases, and immutable logging of all inputs, models, and rationales for insurer and regulator reporting. Rollout is phased, beginning with decision-support alerts before progressing to conditional, automated recommendation execution.

This workflow automates the high-consequence financial trade-off between executing a fuel-consuming evasive maneuver and accepting a conjunction risk. It ingests real-time conjunction data, satellite telemetry, and business parameters (asset value, replacement cost, insurance terms) to model the total expected cost of action versus inaction. By replacing manual spreadsheet analysis and subjective judgment, it provides a data-driven, auditable recommendation, compressing decision cycles from hours to seconds and protecting mission margin through optimized fuel economics.

Implementation requires integrating with SSA feeds (e.g., SOCRATES, CARA), flight dynamics systems, and financial models. The core agent, built on frameworks like LangGraph, orchestrates specialized sub-agents for propagation, cost modeling, and rule evaluation. Critical controls include a mandatory human approval gate for all recommendations, immutable audit logging of all data inputs and model reasoning, and a feedback loop where actual outcomes refine future risk probabilities. This creates a defensible, operational system that balances autonomy with necessary oversight for liability-sensitive environments.