Automated Response Workflow for High-Priority Conjunction Alerts

A high-priority conjunction alert demands immediate action, but manual data gathering, analysis, and notification create dangerous latency. This custom workflow automates the initial response sequence, eliminating hours of repetitive coordination. Upon trigger, it fetches the latest ephemeris and covariance data from SOCRATES or CARA, executes a rapid re-assessment using a high-fidelity propagator, and immediately notifies pre-defined on-call teams via Slack, PagerDuty, and SMS. This automation ensures the right personnel are engaged with a preliminary data package in under five minutes, turning a chaotic manual scramble into a controlled, auditable procedure.

The architecture is built on an orchestrator like LangGraph, which sequences agents for data fusion, probabilistic re-scoring, and communication. The workflow prepares an initial maneuver analysis package, including delta-V options and mission impact estimates, for immediate operator review in a dedicated dashboard. Implementation requires integration with SSA APIs, strict false-positive filtering to prevent alert fatigue, and configurable approval gates before any automated action beyond notification. This system transforms a high-stress operational bottleneck into a reliable, fuel-aware decision-support layer.

When a high-priority conjunction alert is triggered from a source like SOCRATES or an internal screening agent, a central orchestrator immediately activates a multi-agent workflow. This system fetches the latest ephemeris and covariance data from systems like AGI's STK or an internal propagator, executes a rapid re-assessment using high-fidelity probability models, and initiates parallel notifications to pre-defined on-call teams via Slack, PagerDuty, and secure email. The goal is to replace manual, sequential data gathering and communication, which can consume critical hours, with a coordinated, sub-minute automated response sequence that ensures the right personnel are engaged with a validated data package.

The human-in-the-loop gate is a critical control point where the assembled maneuver analysis package—containing probability, fuel costs, and mission impact—is presented for operator approval. Upon approval, the workflow automatically prepares the command sequence and schedules uplink via the ground station software (e.g., EPOCH). If rejected or modified, the case is routed to a senior engineer queue. The entire architecture, built with frameworks like LangGraph for agent orchestration, integrates with mission control systems (MCS) and maintains a full audit trail for insurers and regulators like the FCC, ensuring operational integrity while delivering measurable time-to-decision savings.

Phase 1 establishes the core data and alert ingestion pipeline, integrating commercial SSA feeds (e.g., COMSPOC, LeoLabs) and operator-specific TLE streams into a normalized data lake. A rules-based orchestrator filters and prioritizes alerts using initial probability and miss-distance thresholds, routing 'red' events to a secure notification system (PagerDuty, Slack) and logging all actions for audit. This MVP delivers immediate time savings by automating the initial triage and dissemination of critical alerts, freeing analysts from manual screening.

Phase 2 introduces the AI-powered maneuver analysis agent, built on a framework like LangGraph, which fetches latest ephemeris, executes rapid re-assessments with high-fidelity propagators (e.g., Orekit), and prepares the initial maneuver package. Crucially, this phase implements mandatory human-in-the-loop approval gates via a dedicated operator dashboard before any command sequence is generated. Continuous validation runs in shadow mode, comparing AI recommendations against historical analyst decisions and simulated scenarios to measure accuracy and build operational trust before enabling live autonomous actions.

The governance model must enforce strict controls at each decision point. Automated data fetches from SOCRATES or commercial SSA feeds require validation against quality thresholds before risk re-assessment. AI-generated maneuver packages must pass through a human-in-the-loop approval gate, configurable by probability-of-collision (Pc) thresholds and asset value. Every action is logged to an immutable audit trail, capturing input data, agent reasoning, and outcomes for insurer and regulator review. This control layer transforms automation from a black box into a defensible, operational system.

Rollout should follow a phased adoption, starting with low-risk assets in a shadow mode where the workflow runs in parallel with manual processes, comparing outputs. Phase two introduces the automated alert triage and data preparation for human decision, compressing the 'data-to-brief' timeline. The final phase, enabled by proven accuracy, activates the automated maneuver package generation and approval routing for predefined scenarios. Each phase is governed by KPIs on false-positive rates, time-to-response, and fuel savings, ensuring the automation delivers its promised operational and economic upside before full deployment.

The workflow's resilience hinges on a multi-layered data ingestion and validation pipeline. It ingests streams from SOCRATES, CARA, and commercial SSA feeds, normalizing TLE/OD data while applying confidence scoring and outlier detection. Each data point is tagged with provenance and uncertainty metrics before being passed to the risk-scoring engine. This upfront rigor prevents garbage-in, garbage-out scenarios, ensuring the alerting logic operates on the highest-fidelity operational picture available, which is critical when minutes count.

Exception handling is not an afterthought but a primary control surface. Ambiguous tracks, sensor dropouts, or failed maneuver validations are routed to a dedicated exception queue. Here, specialized agents attempt enrichment and correlation using secondary sources. Unresolved exceptions are escalated with full context to human operators via Slack, PagerDuty, and the primary ops dashboard. This architecture ensures no alert is silently dropped, maintaining system integrity and providing a complete, auditable trail from sensor input to final action—or controlled fallback.