Alerting Rules

This component specifies what to monitor. It defines the precise telemetry data stream the rule will evaluate, such as a specific metric from a time-series database (e.g., Prometheus) or a log pattern from an aggregation system.

• Examples: agent_inference_latency_seconds, http_requests_total{status="500"}, a specific ERROR-level log message pattern.
• Purpose: Isolates the relevant signal from the noise of all system telemetry.

This is the logical heart of the rule. It defines the abnormal state that should trigger an alert, typically by comparing the selected metric against a static or dynamic threshold for a sustained period.

• Static Thresholds: agent_inference_latency_seconds > 2.0
• Dynamic/Baseline Thresholds: Deviation from a rolling average or seasonal pattern.
• Duration/For Clause: FOR 5m ensures the condition is persistent, preventing noise from transient spikes.

These components add context and routing metadata to the fired alert.

• Labels (e.g., severity="critical", agent_type="planner", team="platform") are key-value pairs used for grouping, routing, and silencing alerts. They must be unique for each distinct alert instance.
• Annotations (e.g., summary, description, runbook_url) contain longer human-readable information that explains the alert's impact and suggested remediation steps for the on-call engineer.

These operational parameters control how and when the rule is executed.

• Evaluation Interval: How frequently the rule's condition is checked against the data source (e.g., every 30 seconds). This must align with data scrape intervals.
• Grouping/Alert Aggregation: Rules can be configured to group alerts by labels (e.g., by agent_id) to prevent alert storms. Instead of 1000 alerts for 1000 failing agents, you get one grouped alert summarizing the issue.

This defines where the alert goes once it fires. The rule itself is often decoupled from the notification action via an Alertmanager or similar routing layer.

• Integrations: Alerts are routed to external systems like PagerDuty, Slack, Microsoft Teams, or email based on their labels (e.g., severity).
• Criticality: This enables tiered response, ensuring critical alerts wake someone up while informational ones go to a chat channel.

These are auxiliary controls that manage alert noise and prevent redundant notifications.

• Silences: Temporarily mute alerts matching specific label selectors (e.g., during planned maintenance).
• Inhibition Rules: Configure higher-severity alerts to suppress lower-severity ones from the same source (e.g., a page-level agent_crash alert inhibits a warning-level high_cpu alert for the same agent).

The Golden Signals are four key high-level metrics for monitoring any service: latency, traffic, errors, and saturation. They provide a comprehensive, first-order health check.

• Latency: The time it takes to service a request (e.g., agent response time).
• Traffic: A measure of demand (e.g., requests per second to an orchestrator).
• Errors: The rate of failed requests (e.g., agent execution failures).
• Saturation: How "full" a service is (e.g., queue depth, memory/CPU utilization).

Alerting rules are frequently built on thresholds derived from these signals to detect systemic degradation.

Health checks are automated, periodic probes that verify the operational status and readiness of a software component, such as an individual agent or an orchestration engine.

• Liveness Probe: Determines if the component is running. Failure typically triggers a restart.
• Readiness Probe: Determines if the component is ready to accept work (e.g., dependencies are connected).

Alerting rules can be triggered by the failure of these checks, providing a direct signal of component failure versus a derived metric anomaly.

A Dead Letter Queue (DLQ) is a holding queue for messages that cannot be delivered or processed successfully after a maximum number of retries. In agent systems, this could be inter-agent messages or task assignments that repeatedly fail.

• Purpose: Isolate faulty messages for manual inspection and prevent them from blocking normal processing.
• Alerting Use Case: A cardinal alerting rule monitors the DLQ depth. A non-zero count or a rapid increase triggers an alert, indicating a systemic processing failure or a "poison pill" message that requires engineering intervention.

An error budget is the calculated amount of acceptable unreliability for a service, defined as 1 - SLO. It quantifies how much downtime or errors are "allowed" before violating the service agreement.

• Example: For a 99.9% monthly SLO, the error budget is 0.1%, or approximately 43.2 minutes of downtime per month.
• Alerting Strategy: Sophisticated alerting uses error budget burn rate. An alert fires not just on a binary SLO violation, but when the budget is being consumed at a rate that would exhaust it before the end of the period (e.g., "burning budget 10x faster than allowed"). This enables proactive, risk-based alerting.

Canary analysis is a deployment strategy where a new software version is released to a small subset of users or traffic, and its performance is closely compared to the stable baseline.

• Process: Key metrics (Golden Signals, business metrics) from the canary group and the control group are continuously compared.
• Alerting Integration: Canary analysis platforms run statistical tests to detect regressions. Alerting rules are configured on the output of these tests (e.g., a significant increase in latency or error rate in the canary). This allows for automatic rollback alerts before a faulty agent or orchestrator version impacts the entire system.