Automated Daily Operations Plan Generation and Validation Workflow

Each operations shift traditionally begins with engineers manually synthesizing telemetry health reports, scheduled payload tasks, ground station passes, and planned maneuvers into a coherent plan. This automation ingests these disparate data streams via APIs (e.g., from flight dynamics software like AGI STK, mission planners, and ground station networks), applies conflict-resolution logic, and outputs a validated, time-sequenced plan in minutes. The savings convert directly into higher-value engineering work and enable 24/7 operations without proportional headcount growth.

Manual planning is prone to oversights—scheduling a high-power payload during an eclipse, double-booking a ground station, or missing a conjunction assessment window. The automated workflow embeds domain constraints (power profiles, thermal limits, regulatory rules) directly into its validation engine, using a digital-twin simulation to stress-test the plan. This systematic gatekeeping prevents costly mistakes that can lead to satellite brownouts, missed customer SLAs, or increased collision risk, directly protecting asset value and mission continuity.

The validated daily plan isn't just a PDF for humans; it's the authoritative, machine-readable schedule that triggers all downstream automated systems. It populates the task queue for autonomous payload schedulers, provides the contact timeline for dynamic ground station handovers, and sets the expected state for real-time anomaly detectors. This architecture creates a single source of truth, eliminating coordination drift between separate automation silos and ensuring the entire constellation operates from one synchronized playbook.

A static, manually built plan cannot adapt to real-time changes like a ground station outage or a high-priority re-tasking request. This automated workflow can be designed for agility: when an event triggers a replan (e.g., via a webhook from an external monitoring system), the agent can re-optimize the remaining schedule in near-real-time, preserving high-priority tasks and resolving new conflicts. This turns operations from a batch process into a continuous control loop, capturing time-sensitive revenue opportunities and improving service resilience.

Every automated decision—why a task was scheduled, why a conflict was resolved a certain way, which constraints were applied—is logged with rationale. This creates a defensible audit trail for regulatory compliance (e.g., FCC filings, space safety standards) and internal governance. Furthermore, this structured operational data becomes a training corpus for improving the planning models themselves. The workflow isn't a black box; it's a scalable operating system that gets smarter and more accountable as the constellation grows from dozens to thousands of satellites.

The final, critical impact is on human capital. Instead of spending their shift on clerical synthesis, operators review the AI-generated plan, focusing on edge cases, strategic trade-offs, and long-term fleet health. The workflow includes built-in approval gates and natural language summaries ("Plan highlights three high-priority imaging tasks over Ukraine; all power and thermal margins are green") to facilitate informed oversight. This elevates the mission control team's role, improving job satisfaction and retaining scarce aerospace talent for higher-order problem-solving.

This foundational component continuously ingests and normalizes data from disparate systems: real-time telemetry streams (power, attitude, thermal), the long-term mission calendar, pending payload tasking requests, ground station availability schedules, and planned orbital maneuvers. The workflow's accuracy depends on this unified, time-synchronized data layer, which typically requires custom API connectors to flight dynamics software (e.g., AGI STK), mission planning tools, and ground segment providers.

The core planning logic applies a rules engine and temporal reasoning to identify hard conflicts (e.g., a maneuver during a critical imaging pass, insufficient power for payload activation during eclipse). This agent evaluates millions of potential constraint violations across the fleet—such as thermal limits, data storage capacity, and ground contact overlaps—using solvers like Optaplanner or custom graph-based algorithms. Exceptions are flagged and logged for the validation phase.

A primary AI agent (built on frameworks like LangGraph or CrewAI) acts as the central planner. It takes the fused data and conflict flags, then generates an optimized, time-sequenced plan. Optimization goals are configurable: maximizing revenue-generating task completion, balancing load across the fleet, or minimizing overall energy delta. The agent produces a preliminary plan in a structured format (JSON) ready for downstream validation and human review.

Before release, the draft plan is executed in a high-fidelity digital twin or simulation environment. This safety gate validates that the sequence of commands will not violate spacecraft safety limits when simulated with realistic noise and uncertainty. It checks for off-nominal scenarios, such as a lower-than-predicted solar array output. Any simulation failures trigger a feedback loop to the planning agent for re-planning, creating a closed-loop validation system.

The validated plan is presented to the shift lead via a dedicated operational UI. This interface highlights key activities, shows resource margins (power, data, fuel), and surfaces any lower-priority conflicts that were automatically resolved. The operator can approve the entire plan, reject it (sending it back for re-planning), or make manual overrides. All actions are logged for auditability. Approval triggers the plan to be decomposed into subsystem-specific command sequences.

Upon approval, the master plan is automatically decomposed into subsystem-specific command blocks and dispatched to the appropriate downstream automated systems. This includes: payload tasking sequences sent to the mission planning system, maneuver commands to the flight dynamics team, ground station contact schedules to the network management system, and energy budgets to the power management agent. This component ensures the single source of truth (the daily plan) propagates without manual re-entry errors.

This workflow automates the manual, error-prone 4-8 hour daily shift planning process. The primary value is operational leverage: a single operator can manage a significantly larger fleet. Direct savings come from reduced labor costs for mission planning engineers and the prevention of costly schedule conflicts that lead to missed imaging opportunities or inefficient ground station usage. The secondary value is risk reduction, as automated validation catches constraint violations (e.g., power, thermal, pointing) before commands are uplinked.

The architecture is a multi-agent system, typically orchestrated with a framework like LangGraph or Prefect. Key components include:

• Ingestion Agents: Pull telemetry from databases (e.g., InfluxDB), tasking requests from APIs (e.g., Salesforce), and ground station schedules from providers (e.g., KSAT, AWS Ground Station).
• Synthesis Engine: A constraint-satisfaction solver that merges inputs, respecting power budgets, thermal limits, orbital mechanics, and payload duty cycles.
• Validation Layer: A digital twin or high-fidelity simulator (e.g., AGI STK, custom propagator) that runs the proposed plan to flag violations.
• Approval Gateway: A web interface (often integrated with Slack/MS Teams) presenting the validated plan and highlighted anomalies for human GO/NO-GO.

The workflow's accuracy is dictated by the quality and accessibility of its inputs. Successful implementation requires robust APIs or direct database access to:

• Flight Dynamics Systems (e.g., FreeFlyer, Orekit) for precise ephemeris and event forecasts.
• Telemetry & State Databases for real-time satellite health.
• Mission Planning Software (e.g., AGI STK Scheduler, custom) for existing tasking queues.
• Ground Segment Management tools for antenna availability. Data normalization and schema mapping are critical, as these systems often use disparate formats and update frequencies.

Full autonomy is not the goal; human oversight is required for high-stakes decisions. The workflow must include:

• Approval Gates: Mandatory review for plans exceeding risk thresholds (e.g., involving maneuvers) or during anomaly conditions.
• Explainability Layer: The ability to audit why a task was scheduled or rejected, crucial for operator trust and regulatory compliance.
• Exception Routing: When the solver cannot create a conflict-free plan, it must intelligently route the problematic constraints to a human planner's queue with suggested trade-offs.
• Rollback & Versioning: Ability to revert to a previous validated plan and maintain an immutable audit trail of all plan generations.

A phased, pilot-based rollout de-risks implementation. Phase 1 involves building the synthesis and validation engine for a single satellite, operating in a shadow mode where it generates plans in parallel with human planners for comparison. Phase 2 expands to a small sub-constellation and introduces the approval gateway, allowing for supervised execution. Phase 3 scales to the full fleet and integrates feedback loops for continuous optimization. The pilot window is critical for tuning constraint weights and building operator confidence.

The workflow itself must be instrumented and monitored as a production system. This requires:

• Performance Metrics: Tracking plan generation latency, solver success rate, and human override frequency.
• Fidelity Monitoring: Comparing predicted resource consumption (power, data) in the plan against actual telemetry post-execution to improve model accuracy.
• A/B Testing Framework: The ability to run alternative scheduling algorithms (e.g., different optimization objectives) on a subset of satellites to measure impact on metrics like imaging throughput or energy efficiency.