Multi-Agent Automation for Orbital Data Fusion from Sensor Networks

The operational bottleneck is the manual reconciliation of conflicting observations from Space Fence, optical telescopes, and commercial feeds, which delays catalog updates and degrades risk assessment. A custom multi-agent architecture automates this fusion, where ingestion agents pull raw data, normalization agents apply orbital propagators and uncertainty models, and a correlation agent resolves track-object associations using statistical filters. This workflow eliminates days of analyst reconciliation work per week, ensuring downstream maneuver planning systems operate on the most current and accurate positional data, which is critical for protecting high-value assets.

Implementation integrates with enterprise systems like AGI's STK for propagation and internal mission control software via APIs. The architecture requires robust exception routing for uncorrelated tracks and continuous monitoring for data drift across feeds. Governance is enforced through immutable audit logs of all catalog changes and confidence scores attached to each object state. The result is a production-grade data pipeline that turns fragmented sensor inputs into a trusted, single source of truth, reducing false conjunction alerts by over 30% and compressing data latency from hours to near-real-time.

This workflow automates the critical bottleneck of fusing Space Fence, telescope, and commercial SSA feeds into a single, reliable operational picture. By replacing manual correlation with specialized agents, operators achieve higher tracking accuracy and decision confidence, directly reducing false-alert fatigue and improving the quality of risk assessments. The business value lies in preventing costly satellite losses and operational delays through more precise, automated data hygiene, which is foundational for all downstream maneuver logic.

Implementation integrates LangGraph or a custom orchestrator to manage agent handoffs, with each agent responsible for schema mapping, outlier detection, and confidence scoring. The architecture must include exception routing for unresolved conflicts—such as contradictory observations—to a human-in-the-loop review console. Deployment requires secure API gateways for classified feeds, alongside rigorous monitoring of data latency and agent performance to ensure the fused catalog's integrity for high-stakes conjunction assessments.

This workflow automates the labor-intensive fusion of Space Fence, optical telescope, and commercial SSA feeds into a single, reliable operational picture. It eliminates the manual reconciliation of conflicting observations and schema mismatches that delay risk decisions. The business value is direct: higher catalog accuracy reduces false conjunction alerts, prevents unnecessary fuel-burning maneuvers, and provides the data foundation for confident, automated evasive action. Savings come from reduced analyst screening time and extended satellite operational life through optimized fuel use.

Implementation is phased, starting with a robust ingestion layer using secure APIs and message queues (e.g., Apache Kafka). Specialized agents, built with frameworks like LangGraph, handle schema mapping, outlier detection, and probabilistic correlation. The conflict resolution agent applies business rules and ML models to adjudicate discrepancies, routing only ambiguous cases for human review. The final architecture integrates with downstream systems like CARA or proprietary risk engines, and includes comprehensive observability for data lineage, agent performance, and catalog confidence metrics, ensuring operational trust.

Deploying this fusion workflow starts with a pilot on a single, non-critical data source—like a commercial TLE feed—to validate normalization logic and agent handoffs without impacting live operations. This sandboxed phase establishes baseline performance metrics for latency, data quality, and conflict resolution accuracy. It also allows the engineering team to refine the orchestrator's exception routing and logging before introducing classified or high-fidelity feeds from systems like Space Fence or proprietary optical networks, ensuring the core architecture is stable under load.

Full-scale integration mandates layered controls: a data quality monitor flags sensor drift, an agent performance dashboard tracks resolution confidence, and an immutable audit log records every catalog update for traceability and rollback. Approval gates are embedded before the fused catalog updates live conjunction assessment systems. This phased, controlled approach de-risks the deployment, builds operator trust in the automated agents, and creates a scalable architecture that can incorporate new sensor networks—such as LEO surveillance satellites—without operational disruption.

This workflow automates the labor-intensive, error-prone process of manually fusing orbital data from fragmented sources like Space Fence, optical telescopes, and commercial SSA feeds. By deploying a multi-agent system, operators eliminate the bottleneck of reconciling conflicting observations and delayed catalog updates. The business value is direct: higher-fidelity conjunction alerts reduce false positives, prevent unnecessary fuel-burning maneuvers, and lower the risk of catastrophic collision, protecting multi-million dollar assets. Implementation requires agents for ingestion, schema normalization, confidence scoring, and conflict resolution, orchestrated by a central controller.

Implementation integrates with LangGraph or a custom orchestrator to manage agent handoffs, state, and exception routing. Each source-specific ingestion agent handles API protocols and raw data parsing, outputting to a normalized schema. The fusion agent employs rule-based and ML models to resolve conflicts, weighted by source pedigree and recency. The updated catalog feeds downstream systems like CARA or proprietary risk engines. Critical controls include human review gates for low-confidence correlations, immutable audit logs for data lineage, and continuous monitoring of agent performance and data drift to ensure operational reliability.