Automated Anomaly Reporting to SOCRATES or CARA

Manual conjunction reporting to services like SOCRATES (Space Data Association) or NASA's CARA is a repetitive, time-sensitive operational bottleneck. Analysts must extract data from internal systems, format it to strict schemas, and submit it within tight windows, often under high-pressure conjunction events. Automating this workflow eliminates this administrative toil, reduces human error, and ensures operators meet data-sharing obligations, directly improving the quality and speed of the collaborative space safety ecosystem. The savings come from reallocating skilled personnel from data entry to higher-value risk analysis and response coordination.

Implementation requires a LangGraph-based orchestrator that ingests CDMs from internal propagators or commercial feeds. A validation agent filters false positives using domain rules and ML models. A report generator, powered by an LLM with RAG over regulatory templates (e.g., FCC, IADC guidelines), drafts the submission. The workflow integrates a mandatory human review gate for high-risk events before secure API submission to SOCRATES or CARA. Critical controls include immutable audit logging, exception routing for data discrepancies, and configurable approval thresholds based on risk scores to maintain operational governance.

This workflow automates a critical but repetitive operational bottleneck: the manual drafting, formatting, and submission of conjunction data messages (CDMs) to external space traffic coordination services like SOCRATES (Space Data Association) or NASA's CARA. The business value is direct labor savings for flight dynamics teams and a significant reduction in reporting latency, which improves the quality and timeliness of the shared orbital data ecosystem. Faster, more accurate reporting enhances overall collision avoidance efficacy for all operators.

Implementation integrates with existing flight dynamics software (e.g., AGI's STK, internal propagators) via APIs or file monitors. The orchestrator, built on a framework like LangGraph, manages the sequential agent workflow. Critical controls include validation rulesets to filter low-confidence data, mandatory human-in-the-loop approval gates for all submissions, and immutable audit logs capturing the data lineage, agent decisions, and reviewer actions for regulatory defensibility.

This workflow automates a critical yet repetitive operational bottleneck: the manual compilation of technical conjunction data into standardized reports for data-sharing services like SOCRATES (Space Data Association) or NASA's CARA. For satellite operators, each high-risk conjunction event triggers a labor-intensive process of extracting ephemeris, covariance, and probability data from internal systems, formatting it against regulatory templates, and submitting it via web portals or APIs. Automating this process saves analyst hours per event, reduces human error in critical safety communications, and ensures operators meet contractual data-sharing obligations and regulatory expectations for transparency, directly strengthening their standing in the space community.

Implementation follows a phased integration approach, starting with the ingestion pipeline for CDMs from internal conjunction assessment tools. The core orchestrator, built with frameworks like LangGraph, manages the workflow: it retrieves the correct reporting template via RAG from IADC or service-specific guidelines, populates it using an LLM agent trained on technical orbital language, and routes the draft through a human-in-the-loop approval gate in the operator's existing mission control system. Critical controls include versioning for audit trails, exception handling for data gaps, and configurable business rules to determine report priority based on risk score, ensuring the system enhances—rather than disrupts—high-reliability operations.

Manual conjunction reporting is a high-latency, error-prone bottleneck that delays the shared space picture. This custom workflow automates the ingestion of validated conjunction data messages (CDMs), applies regulatory templates, and drafts narrative reports for SOCRATES or CARA submission. It eliminates hours of analyst copy-paste work per alert, ensures consistent formatting per IADC guidelines, and accelerates data-sharing cycles from days to minutes, directly improving collective orbital safety and operator compliance posture.

Implementation integrates with your existing SSA pipeline and risk engine via APIs. The orchestrator, built on LangGraph, manages state, retrieves the latest regulatory frameworks via RAG, and calls an LLM agent for context-aware drafting. Critical controls include mandatory human-in-the-loop approval gates for all submissions, immutable audit trails of all data and decisions, and configurable exception routing for low-confidence or high-severity events. This creates a defensible, scalable reporting layer that meets operator liability and data-sharing obligations.

This workflow automates a critical yet repetitive compliance task: generating and submitting standardized conjunction reports to external space situational awareness (SSA) services like SOCRATES or CARA. It eliminates the manual effort of extracting data from internal systems, formatting it to specific schemas, and managing submission deadlines. The operational upside is direct labor savings for flight dynamics teams, improved reporting timeliness for the broader SSA ecosystem, and reduced risk of non-compliance with data-sharing agreements, which can affect licensing and insurance.

Implementation requires integrating the orchestrator (e.g., LangGraph) with your conjunction assessment pipeline and the external APIs. The LLM agent uses RAG against reporting guidelines to ensure format compliance. Critical controls include data sanitization to remove proprietary orbital details, mandatory human-in-the-loop approval for all submissions, and immutable audit logging of each report's lifecycle. This creates a scalable, defensible reporting architecture that turns a manual compliance burden into an automated operational process.