Automating Communication with Other Operators for Coordination

Manual operator-to-operator coordination for mutual-risk conjunctions involves drafting emails, finding correct POCs, and waiting for responses—a process that can take 24-72 hours. An automated agent drafts templated, data-rich messages (using CDM data) and sends them via secure channels (e.g., encrypted API, Space Data Association portals) immediately upon risk confirmation. This reduces the critical path to a coordinated response, shrinking the window for collision risk to escalate.

Manual transcription of conjunction data messages (CDMs) into emails or forms introduces risk of misstated parameters (e.g., misspoken TCA, incorrect object IDs). The automated workflow ingests CDMs directly, uses structured templates to populate fields, and validates data consistency before sending. This ensures technical accuracy in all communications, preventing misalignment that could lead to conflicting maneuver decisions.

For low-to-medium risk events, the AI agent handles the entire communication loop: sending the initial notice, parsing the counterparty's response (accepting, proposing alternative, or escalating), and updating internal tracking systems. This frees human operators from ~70% of routine coordination tasks, allowing them to focus on high-stakes negotiations, complex multi-asset scenarios, and strategic fleet management.

Every automated message, response, and internal decision is logged with timestamps, data snapshots, and agent reasoning. This creates a compliant audit trail for regulators (FCC, NOAA), insurers, and internal reviews. The workflow integrates with systems like ServiceNow or Jira for ticketing, ensuring end-to-end traceability from risk detection through coordination closure, which is critical for liability protection and demonstrating due diligence.

The agent can be programmed with negotiation protocols (e.g., IADC guidelines for right-of-way) and cost-benefit logic. It can propose maneuver trade-offs, suggest fuel-efficient solutions, or automatically cede right-of-way based on pre-defined business rules (e.g., older satellite maneuvers first). This brings a data-driven, consistent approach to negotiations, often leading to more optimal collective outcomes than ad-hoc human discussion.

Manual coordination does not scale for operators managing hundreds of satellites, where multiple conjunctions may occur daily. This automated workflow, built with orchestration frameworks like LangGraph, can manage dozens of simultaneous communication threads, correlate responses, and flag conflicts. It enables a single operations team to manage fleet-wide coordination that would otherwise require a small army of analysts, directly supporting the economic model of large LEO constellations.

An LLM agent ingests conjunction data (CDMs), operator contact protocols, and regulatory templates to draft initial coordination messages. It standardizes technical details (TCA, miss distance, probability), proposes potential maneuver windows, and frames the communication according to established space safety norms (e.g., IADC guidelines), ensuring clarity and reducing miscommunication risk from the outset.

The workflow integrates with approved, encrypted communication channels (e.g., SOCRATES API, Space Data Association portals, PGP-encrypted email). An orchestration agent routes the drafted message based on the recipient's registered preferences and security requirements, tracks delivery receipts, and manages retries. This eliminates manual lookups and insecure ad-hoc emails, creating a defensible audit trail for all outgoing communications.

Incoming responses are ingested and parsed by a dedicated agent using NLP to classify intent: agreement, counter-proposal, request for data, or escalation to human. The agent extracts key parameters (proposed new TCA, alternative delta-V) and updates the internal conjunction case file. This automates the triage of the communication loop, allowing the system to proceed with negotiation or immediately flag complex cases.

For simple counter-proposals, an AI agent evaluates the other operator's suggested parameters against fuel budgets, mission constraints, and updated risk scores. It can generate a counter-counter-proposal or an acceptance message, maintaining a structured negotiation thread. This agent operates within pre-defined guardrails (e.g., max delta-V concession) and escalates any proposal falling outside bounds, automating the bulk of routine coordination.

The workflow is built with deliberate escalation triggers. Complex negotiations, ambiguous responses, or interactions with high-value assets automatically route the full thread to a human operator's dashboard with a summarized context and recommended actions. This ensures critical decisions retain human oversight while freeing operators from managing every routine exchange, optimizing their time for high-stakes judgment.

Every automated action—message draft, send time, parsed response, agent reasoning—is logged to an immutable record linked to the conjunction event. This creates a compliance-ready package for regulators (FCC, NOAA), insurers, and internal reviews, proving due diligence in coordination efforts. The log integrates with broader mission data, providing end-to-end traceability from risk detection through resolution.