AI Workflow for Graceful Degradation Management

Instead of entering a permanent safe-hold or requiring immediate, high-risk recovery commands from Earth, the workflow's goal-replanning agents dynamically adjust mission parameters. This allows a damaged probe or rover to continue operating for months or years longer than a static contingency plan, directly preserving the multi-million or billion-dollar capital investment and securing additional data.

Manual recovery of a degraded asset requires round-the-clock ground team effort, intensive simulation, and precious Deep Space Network (DSN) time. This autonomous workflow absorbs that operational burden, executing validated reconfiguration sequences onboard. The result is a dramatic reduction in ground control costs and freeing of DSN assets for other missions.

Following a failure (e.g., loss of a reaction wheel, solar array), agents model the new power, thermal, and pointing constraints. They then re-allocate these finite resources to the highest-priority remaining objectives, ensuring every watt-hour and bit of downlink bandwidth is spent on the most valuable science or engineering data, maximizing ROI from the impaired system.

A high-profile mission failure has significant consequences for funding, partner confidence, and public perception. By demonstrating an autonomous capability to 'fight through' failures and still deliver value, this workflow de-risks the entire mission portfolio. It turns a potential public relations crisis into a showcase of resilient engineering.

With light-speed delays measured in minutes to hours, ground-based fault diagnosis and response is inherently slow. This onboard workflow detects, diagnoses, and initiates a contingency plan within seconds or minutes, containing issues before they cascade. This speed is critical for preventing a partial failure from becoming a total loss.

The investment in building this custom workflow—using frameworks like LangGraph for orchestration and NASA's F Prime for flight software—creates a reusable autonomy core. This core can be adapted for future missions, reducing non-recurring engineering costs and establishing a competitive advantage in bidding for high-risk, high-value exploration contracts.

This agent continuously ingests telemetry from all subsystems (power, propulsion, thermal, instruments). It runs lightweight anomaly detection models to identify the root cause and scope of a failure. Its output is a structured fault report detailing affected components, severity, and remaining functional capacity, which triggers the goal-replanning sequence.

The core reasoning engine. It ingests the fault report and the current mission goal stack (e.g., 'traverse 1km,' 'analyze 5 rock targets'). Using a constraint-satisfaction or optimization algorithm, it generates a new, viable mission plan that maximizes scientific or operational value within the new resource envelope (reduced power, mobility, data bandwidth).

Once a new goal plan is approved, this agent breaks it into executable tasks and schedules them against precise power, thermal, and timing budgets. It dynamically deconflicts instrument usage and communication windows, ensuring the degraded system does not violate safe operating limits. Integrates with onboard time-tagged command queues.

A high-criticality agent that manages the transition to and from contingency states. If the fault is severe, it executes predefined safe-mode entry sequences. For reconfigurable systems, it sends commands to power-cycle redundant units, reroute data buses, or modify control law parameters, acting on directives from the Diagnosis Agent.

A logging and synthesis agent that compiles a complete, timestamped record of the fault event, the replanned actions, and all subsequent system performance data. This structured log is prioritized for downlink, providing ground teams with an auditable trail for forensic analysis and updating the sim-to-real validation models.

Not all decisions are fully autonomous. This is a configurable control layer that routes major replanning decisions (e.g., abandoning a primary objective) to ground control for approval during the next communication pass. It manages the handshake protocol, presenting the agent's rationale and awaiting a GO/NO-GO or modified command sequence.

Owns the overall graceful degradation architecture, defining the system models, redundancy maps, and permissible degraded states. Responsible for integrating the re-planning agents with the core flight software, fault management system, and vehicle health models. Ensures the workflow's decisions are physically feasible and within the spacecraft's safety envelope.

Develops and validates the goal-replanning logic and resource optimization agents. Uses frameworks like LangGraph to orchestrate the multi-agent workflow that re-evaluates mission objectives against remaining capabilities (power, data, propulsion). Defines the reward functions and constraints for the re-planning algorithms to maximize continued scientific or operational ROI.

Approves the transition into a degraded operations mode and validates the autonomous re-plan. Maintains ultimate command authority, with the workflow providing recommended courses of action. Defines the telemetry and alerting requirements for ground-based situational awareness during contingency operations, ensuring the team can monitor and override if necessary.

Defines the scientific value hierarchy used by the re-planning agents to prioritize objectives (e.g., imaging specific terrain vs. atmospheric sampling). Works with the autonomy lead to encode this into the agent's decision logic. Reviews the science data captured in the degraded state to assess the workflow's effectiveness in preserving mission ROI.

Responsible for testing the graceful degradation workflow against a digital twin in high-fidelity simulation. Creates fault injection scenarios (solar array failure, instrument loss, comms degradation) to validate the agent's responses and ensure they do not violate system safety limits. Manages the sim-to-real validation pipeline before flight software updates.

Builds the ground-based tools to ingest, visualize, and explain the autonomous re-planning decisions. Ensures the workflow's audit trail—including the rationale for de-scoped objectives and resource re-allocations—is logged and accessible for post-event analysis and reporting to stakeholders. Integrates this data with mission assurance systems.