Health Score

The foundation of a health score is a set of quantifiable performance indicators. These typically include the Golden Signals of monitoring: Latency (response time), Traffic (request volume), Error Rate (failed operations), and Saturation (resource utilization like CPU or memory). A weighted aggregation of these real-time values forms the primary numerical baseline.

For physical agents like Autonomous Mobile Robots (AMRs), the health score incorporates hardware and environmental telemetry. Key inputs include:

• State of Charge (SoC): Current battery level.
• Battery Degradation: Long-term capacity loss.
• Motor/Actuator Temperatures: Overheating indicators.
• Sensor Status: Lidar, camera, or IMU functionality checks.
• Connectivity Strength: Signal quality for Wi-Fi or 5G.

Health scores are validated through active diagnostic probes. These are programmatic checks that query the agent's state:

• Liveness Probe: Confirms the agent's software process is running (e.g., can it respond to a ping?).
• Readiness Probe: Verifies the agent is fully initialized and ready to accept tasks (e.g., dependencies loaded).
• Heartbeat Signal: A periodic status message; its absence triggers a health score penalty.

Advanced health scoring integrates predictive analytics and anomaly detection. This moves the score from reactive to proactive by including:

• Remaining Useful Life (RUL): Forecasts for critical components.
• Anomaly Detection Flags: Statistical deviations from normal operational baselines for metrics or log patterns.
• Configuration Drift: Detection of unauthorized changes from a defined 'golden' system image.

Not all metrics contribute equally. The health score algorithm applies configurable weights to each component based on business criticality. For example, a high error rate may be weighted more heavily than slightly elevated latency. The aggregation function (e.g., weighted mean, minimum of critical components) determines how partial failures impact the overall score.

The final score may be adjusted by external context. This includes:

• Zone Management Protocols: Is the agent in a restricted or high-priority area?
• Task Criticality: Is the agent executing a high-priority mission?
• Fleet-Wide State: Are other agents also experiencing similar issues, suggesting a systemic problem rather than an individual fault?

A programmatic interface that allows an orchestration system to query the operational status and readiness of an individual agent or service. It is the primary mechanism for pulling structured diagnostic data to calculate a Health Score.

• Key Function: Exposes endpoints (e.g., /health, /ready) that return standardized JSON responses with status codes and component-level details.
• Implementation: Often includes checks for database connectivity, memory usage, subsystem liveness, and dependency status.
• Use Case: An orchestration middleware polls this API on each agent every 30 seconds to populate the fleet-wide health dashboard.

Two distinct diagnostic mechanisms used in containerized and distributed systems to manage an agent's lifecycle within a cluster.

• Liveness Probe: Determines if an agent's process is running. Failure triggers a restart. Example: A simple TCP socket check on the agent's main port.
• Readiness Probe: Determines if an agent is fully initialized and ready to accept work. Failure removes it from load balancers. Example: A check that verifies all sensor drivers are loaded and calibrated.
• Relation to Health Score: These probes provide binary (pass/fail) inputs that are critical weighted components of a comprehensive Health Score.

A data-driven maintenance strategy that uses telemetry and machine learning models to forecast equipment failures before they occur, enabling repairs during planned downtime.

• Core Inputs: Analyzes time-series data from sensors (vibration, temperature, motor current) and operational logs.
• Key Output: Remaining Useful Life (RUL) estimation, a predictive metric often fed directly into an agent's Health Score calculation.
• Business Impact: Reduces unplanned downtime by over 30% and extends asset lifespan by scheduling maintenance based on actual wear, not fixed intervals.

The continuous flow of operational data from agents to a central system, which is then analyzed to identify deviations from normal behavior.

• Telemetry Stream: Includes metrics (CPU, memory), logs, events (task started/completed), and sensor readings. Flows into a metrics pipeline for aggregation.
• Anomaly Detection: Applies statistical models or ML (like isolation forests) to this stream to flag unusual patterns—e.g., a gradual increase in motor temperature or a spike in network latency.
• Health Score Integration: A detected anomaly directly lowers the Health Score, triggering alerts for operator investigation before a hard failure occurs.

Design principles that ensure a system maintains partial functionality during partial failures, and can automatically switch to backup components.

• Graceful Degradation: An agent with a failed sensor might continue navigation using alternative sensors, reporting a reduced-capability state. This is reflected in a lowered but non-zero Health Score.
• Failover State: The automatic transition of workload from a primary agent (with a failing Health Score) to a standby agent. Orchestration middleware manages this transition based on health thresholds.
• Circuit Breaker Pattern: A related stability pattern that prevents cascading failures by stopping calls to a repeatedly failing service, allowing it time to recover.

The reliability targets and key metrics used to measure the health and performance of services at scale.

• Golden Signals: The four key monitoring metrics defined in Site Reliability Engineering: Latency, Traffic, Errors, and Saturation. These are primary inputs for calculating a service's Health Score.
• Service Level Objective (SLO): A target value for a Golden Signal, e.g., "99.9% of navigation requests under 100ms." Health Scores are often designed to reflect SLO compliance.
• Operational Use: Breaching an SLO for a sustained period should cause a significant drop in the system's Health Score, prioritizing it for engineering intervention.