Orchestrating Global Conjunction Data Exchanges for Space Traffic Management

The operational bottleneck is the manual, trust-based exchange of sensitive conjunction data between competing operators, which delays risk assessment and increases collision probability. A custom workflow automates this as a neutral hub, ingesting standardized Conjunction Data Messages (CDMs) from operators and agencies via secure APIs. An orchestrator validates, anonymizes operator identities, and enforces data-sharing agreements, transforming a fragile diplomatic process into a reliable, high-throughput system. This directly reduces coordination latency from days to minutes, a critical factor for last-ditch maneuvers.

Implementation integrates with existing operator systems like AGI's STK or internal mission control software via adapters. The architecture's core is a rules-and-agentic layer that applies routing logic—only parties with assets in the relevant orbital regime receive alerts—preserving competitive secrecy. Controls include cryptographic data provenance, human-in-the-loop approval gates for non-standard data releases, and full audit trails for regulatory compliance (e.g., FCC, UNOOSA). This creates a scalable, defensible coordination layer that turns shared situational awareness into a operational asset.

This architecture automates the high-friction, manual exchange of sensitive conjunction data between operators, a critical bottleneck for global space safety. It eliminates days of negotiation and data-wrangling by providing a standardized, secure pipeline. The business value is direct: reducing collision risk through faster, more comprehensive data sharing, which protects multi-billion-dollar assets and preserves orbital slots. The system ingests raw data from operators, military feeds (e.g., Space Fence), and commercial SSA providers, applying governance before distribution.

Implementation requires integrating with operator systems via APIs or secure file transfer, using agents for schema normalization, confidence scoring, and GDPR/ITAR-compliant anonymization. The governance engine enforces data-sharing agreements, flags non-compliant submissions, and routes exceptions for human legal review. The distribution agent uses RAG over orbital regimes to route alerts only to affected parties, preventing information overload. Observability is built in, tracking data lineage, alert delivery, and response latency to prove the hub's operational value and auditability for regulators.

Phase 1 establishes the core ingestion and governance layer. The workflow ingests raw Two-Line Element (TLE) and Orbit Determination (OD) data from operators, military, and commercial feeds via secure APIs. An orchestrator validates schemas, strips identifying metadata for anonymization, and applies confidence scoring to create a trusted, unified object catalog. This foundational layer automates the manual data-wrangling bottleneck, providing a single source of truth and enabling downstream distribution. Savings come from eliminating 70-80% of analyst time spent on data fusion and normalization.

Phase 2 implements agentic distribution and operational integration. A routing agent evaluates the anonymized conjunction data against subscriber orbital regimes and service-level agreements. It triggers prioritized, formatted alerts via SOCRATES/CARA-compatible APIs or direct secure channels. The architecture integrates approval gates for high-risk data shares and observability dashboards for audit trails. This phase automates the manual alert dissemination process, compressing coordination timelines from hours to minutes and directly improving global collision avoidance response times. Implementation uses LangGraph for agent orchestration, with controls for data sovereignty and exception handling.

A neutral data exchange automates the ingestion and secure distribution of conjunction data across competing operators, eliminating manual, bilateral data-sharing bottlenecks. The business value is direct: reduced collision risk through faster, more comprehensive awareness, which protects multi-billion-dollar assets and preserves orbital slots. Implementation requires a robust ingestion layer for SSA feeds (e.g., Space-Track, commercial providers), immediate anonymization logic to strip operator-identifying metadata, and a rules engine to route alerts based on orbital regimes and object identifiers, ensuring relevant parties receive critical data without exposing competitive posture.

Governance is enforced by a central engine that codifies data-sharing agreements, controlling what data is shared and with whom. The architecture integrates with existing operator systems via secure APIs, requiring controls for audit trails, non-repudiation, and exception handling for data conflicts. A phased rollout starts with a trusted pilot group, validating anonymization and routing logic before scaling. This creates a production-grade system that turns fragmented SSA data into a coordinated, operational asset, reducing collective risk and establishing a scalable model for international space traffic management.

This workflow automates the critical bottleneck of manually sharing sensitive orbital data across sovereign and competitive operators. It enables faster, more comprehensive risk assessment by creating a trusted, automated pipeline for conjunction data. The operational upside comes from reducing collision risk through earlier, data-rich warnings and eliminating the days-long delays of manual data requests and legal reviews. Savings are realized in avoided satellite loss, lower insurance premiums, and reduced operational overhead for coordination.

Implementation requires a multi-tenant cloud architecture with strict data sovereignty controls, likely using confidential computing enclaves for processing. The core orchestrator, built on a framework like LangGraph, manages agents for ingestion (parsing CDMs/OD from SOCRATES, CARA), anonymization (scrubbing operator IDs, mission data), and policy-based routing. Integration with identity providers and PKI ensures authenticated, encrypted exchanges. Controls include automated compliance checks against data-sharing agreements, human-in-the-loop approval gates for policy exceptions, and immutable audit logs for regulatory defense and liability protection.