Real-Time Payload Re-tasking Based on Event Detection Workflow

Manual tasking for events like wildfires or ship movements involves analysts spotting the event, checking satellite availability, and drafting commands—a process taking hours. A custom AI workflow ingests news, weather, and AIS feeds, evaluates satellite visibility and memory, and generates validated re-tasking commands in under 10 minutes. This reduction in decision-to-collection latency is the difference between actionable intelligence and irrelevant, after-the-fact imagery.

Satellites in standby or between scheduled tasks represent unused, depreciating assets. An autonomous re-tasking workflow creates a dynamic, high-margin fill business. By automatically assigning these idle slots to premium, event-driven collection tasks, operators can increase effective constellation utilization by 15-25%, directly translating to new revenue streams without additional capital expenditure on hardware.

Manual 24/7 monitoring of global event feeds is labor-intensive and prone to oversight. This workflow automates the detection and initial triage, routing only validated, actionable opportunities to a human operator for final approval. It eliminates the constant low-signal noise, allowing a small team to manage a global monitoring service, reducing operational headcount requirements by an estimated 2-3 FTE per shift while improving decision quality.

Implementing this workflow builds the core orchestration layer for broader constellation autonomy. It requires integrating event ingestion APIs (e.g., GDACS, MarineTraffic), a LangGraph-based multi-agent system for evaluation and planning, a digital twin for command validation, and secure interfaces to the mission planning system (e.g., AGI STK, FreeFlyer) and ground station network. This architecture becomes the reusable pattern for other automated workflows like dynamic scheduling or collision avoidance.

Autonomous re-tasking introduces risk if unchecked. The custom workflow embeds governance at each step: confidence scoring on event detection, constraint checking against satellite safemodes and thermal limits, command sequence validation via a digital twin, and a mandatory human-in-the-loop approval gate for high-value assets or novel scenarios. This creates an audit trail for every decision, ensuring operational safety and regulatory compliance while maintaining speed.

A production build follows a phased approach: 1) Pilot with a single satellite and two event types (e.g., wildfires, AIS gaps), using cloud-based agents and a sandbox mission control system. 2) Scale to the full constellation, integrating with the production flight dynamics and mission planning system (e.g., SAP ERP for space, or custom MCS). 3) Expand event sources and connect to customer portals for automated tasking requests. Observability is built on OpenTelemetry traces for every agent decision.

This specialized agent continuously monitors and parses external data feeds (e.g., news APIs, weather services, AIS streams, social media) for predefined event signatures (wildfire ignition, unauthorized ship movement, geopolitical incidents). It extracts geospatial coordinates, timestamps, and confidence scores, transforming raw data into structured 'event tickets' for the orchestration layer. Implementation requires robust API management, geocoding, and deduplication logic to filter signal from noise.

A real-time query agent that interfaces with the Flight Dynamics System (FDS) and Mission Planning database. For each event ticket, it evaluates which satellites in the constellation have the required sensor (e.g., SAR, multispectral), are within visibility windows, possess sufficient onboard power/memory, and are not committed to higher-priority tasks. This component performs dynamic capacity modeling, often requiring integration with systems like AGI's STK or custom propagators.

An LLM or rules-based agent that takes the event and satellite data to draft a compliant command sequence. It references satellite command dictionaries and operational constraints (thermal limits, pointing budgets) to generate safe instructions. A digital twin validation layer then simulates the command sequence against the current spacecraft state to catch errors before uplink, a critical safety gate. This integrates with tools like NASA's OpenMCT or proprietary simulators.

This agent manages the physical link. Upon command validation, it interfaces with ground station service APIs (e.g., AWS Ground Station, KSAT) to schedule the earliest possible contact window for the target satellite, books the antenna, and prepares the uplink package. It must resolve scheduling conflicts across the fleet and optimize for cost and latency, requiring a bidding/negotiation logic layer for shared commercial networks.

Not all re-tasking is fully autonomous. This control plane routes proposed actions through configurable approval gates based on event confidence, satellite criticality, or customer SLA. It presents the rationale, opportunity cost, and validation results to a human operator via a dedicated console, with options to approve, modify, or reject. All decisions and overrides are logged to an immutable audit trail for compliance and A/B testing of agent performance.

The workflow's nervous system. It tracks every event from detection to image delivery, emitting metrics for latency, success rate, and resource consumption. It automatically tags data products with the triggering event ID for customer billing and SLA credit determination. Crucially, it closes the loop by feeding outcome data (e.g., image quality, task success) back to the detection agents to refine future event scoring and prioritization logic.