Natural Language Querying of Constellation Status Workflow

Operators waste hours daily writing complex SQL queries and cross-referencing dashboards to answer status questions. This workflow automates that retrieval via a conversational interface, using a RAG architecture over telemetry (e.g., InfluxDB), tasking (e.g., legacy planning software), and orbital (e.g., AGI STK) databases. The business value is direct: it reduces the time-to-answer for critical operational questions from minutes to seconds, allowing a smaller team to manage a larger, more complex constellation by removing a key cognitive bottleneck.

Implementation requires a LangGraph or CrewAI orchestrator to manage the multi-step retrieval, synthesis, and response generation. The agent must integrate with APIs or direct database connectors, handle ambiguous queries with clarification logic, and enforce strict access controls. A human-in-the-loop review layer is essential for high-stakes queries before any command generation. This architecture turns fragmented data into an operational intelligence layer, directly improving decision velocity and reducing the risk of oversight during complex anomaly investigations.

This workflow automates the high-friction process of an operator manually logging into separate systems—a telemetry database like InfluxDB, a tasking platform like AGI's STK, and an orbital database—to piece together an answer to a simple status question. The operational upside is measured in minutes saved per query, which compounds across a 24/7 operations center, reducing cognitive load and accelerating decision velocity during anomalies or busy pass periods. The solution requires a multi-agent architecture to decompose queries, retrieve from specialized sources, and synthesize a coherent, cited answer.

Implementation integrates LangGraph or CrewAI for agent orchestration, with each agent using a RAG pipeline over its dedicated vector store (e.g., Pinecone, Weaviate) populated from system APIs. The Validator Agent is critical, applying rule-based checks (e.g., 'battery cannot be 100% during eclipse') and confidence scoring before presenting the answer. The workflow must integrate with existing SSO and audit systems, logging every query and data source for traceability. Rollout requires a phased pilot with a subset of satellites and a human-in-the-loop review gate before full autonomous deployment to the ops floor.

This workflow automates the high-latency, manual process of operators logging into multiple specialized systems (e.g., flight dynamics software, telemetry databases, mission planners) to answer status questions. By implementing a Retrieval-Augmented Generation (RAG) layer over these siloed data sources, you cut situational awareness queries from minutes to seconds. The operational upside is direct: reduced cognitive load on engineers, faster decision-making during anomalies, and the ability to scale constellation oversight without linearly increasing headcount. The architecture must integrate with systems like AGI's STK, KubOS, or custom MCS databases via secure APIs.

Implementation follows a phased 12-week sprint: W1-4 for data connector development and vectorization of historical telemetry logs; W5-8 for building the LangGraph orchestration with query routing, fallback logic, and integration with approval gates for command-related queries; W9-12 for rigorous testing, including synthetic query generation and integration with existing mission control dashboards. Critical controls include query logging for audit, confidence scoring on answers, and automatic escalation for low-confidence or safety-critical responses. The final deliverable is a containerized service deployed within the mission network, accessible via web UI or Slack/Microsoft Teams bot.

Operational governance begins with strict access controls and audit logging for all queries, ensuring traceability for compliance and security reviews. The RAG pipeline must be validated against a curated set of known-good orbital and telemetry questions before live deployment, with accuracy thresholds defined for each data source (e.g., NORAD TLEs, proprietary telemetry databases). A human-in-the-loop review phase is mandatory, where ambiguous or high-confidence-low queries are routed to a senior operator dashboard for validation, preventing the propagation of incorrect orbital calculations or health assessments.

Rollout should follow a phased, satellite-by-satellite or team-by-team approach, starting with non-critical data domains like pass schedules before expanding to power and subsystem health. Integration points are critical: the query orchestrator must connect via secure APIs to legacy systems like AGI's STK for propagation, MQTT streams for live telemetry, and ServiceNow for ticket creation from unresolved queries. Continuous monitoring of query latency, cache hit rates, and user correction feedback drives iterative model retraining and prompt engineering, ensuring the system adapts to real operator phrasing and emerging anomaly patterns without degrading core mission operations.

This workflow automates the repetitive, time-consuming task of manually querying multiple specialized databases (e.g., NORAD TLEs, telemetry stores, mission planning systems) to answer routine operational status questions. The operational upside comes from reducing operator cognitive load and decision latency, which directly improves fleet responsiveness and allows a single engineer to manage more assets. Implementation requires a Retrieval-Augmented Generation (RAG) architecture that integrates vector search over historical and real-time data streams, an LLM orchestrator for query interpretation and synthesis, and strict access controls tied to existing IAM systems.

In practice, implementation involves deploying the orchestrator (e.g., LangGraph) to manage the query decomposition, parallel retrieval, and answer generation logic. Critical controls include query logging for audit trails, confidence scoring on retrieved data to trigger human review for low-confidence answers, and integration with approval gates for any query that could initiate a command. The system must be monitored for hallucination rates and retrieval accuracy, with a rollback path to traditional dashboards. This architecture connects to enterprise systems like SAP for asset management or ServiceNow for ticketing follow-up actions.