feat(ai): enhance intelligence status and add trend prediction
AIStatusIndicator: - Now shows BOTH patrol findings AND baseline anomalies - Displays even when only anomaly detection is active (no patrol) - Badge count includes both findings + anomalies - Tooltip provides detailed breakdown by severity Trend Prediction (backend): - Add TrendPrediction struct for resource exhaustion forecasting - CalculateTrend() uses linear regression on sample history - Predicts days until resource is full (or if declining/stable) - Severity: critical (<7 days), warning (<30 days), info (>30 days) - Human-readable descriptions like 'full in ~2 weeks (+0.5% per day)' This creates a more cohesive intelligence experience where anomaly detection works independently of the pro/patrol features, making value visible immediately to all users.
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rcourtman
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2 changed files with 255 additions and 29 deletions
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@ -1,13 +1,14 @@
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/**
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* AIStatusIndicator - Subtle header component showing AI patrol health
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* AIStatusIndicator - Subtle header component showing AI patrol health and anomalies
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*
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* Design: Minimal presence when healthy, highlighted when issues detected.
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* Design: Minimal presence when healthy, highlighted when issues or anomalies detected.
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* Clicking navigates to the Alerts page where AI Insights are displayed.
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*/
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import { createResource, Show, createMemo, onCleanup } from 'solid-js';
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import { useNavigate } from '@solidjs/router';
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import { getPatrolStatus, type PatrolStatus } from '../../api/patrol';
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import { useAllAnomalies } from '@/hooks/useAnomalies';
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import './AIStatusIndicator.css';
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export function AIStatusIndicator() {
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@ -25,10 +26,25 @@ export function AIStatusIndicator() {
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{ initialValue: undefined }
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);
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// Refetch every 30 seconds with proper cleanup
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// Get anomaly data (also polls every 30 seconds via the hook)
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const anomalyData = useAllAnomalies();
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// Refetch patrol status every 30 seconds with proper cleanup
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const intervalId = setInterval(() => refetch(), 30000);
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onCleanup(() => clearInterval(intervalId));
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// Count anomalies by severity
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const anomalyCounts = createMemo(() => {
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const anomalies = anomalyData.anomalies();
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const counts = { critical: 0, high: 0, medium: 0, low: 0 };
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for (const a of anomalies) {
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counts[a.severity]++;
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}
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return counts;
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});
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const totalAnomalies = createMemo(() => anomalyData.count());
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const hasIssues = createMemo(() => {
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const s = status();
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if (!s) return false;
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@ -41,6 +57,16 @@ export function AIStatusIndicator() {
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return s.summary.watch > 0 && !hasIssues();
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});
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const hasAnomalies = createMemo(() => {
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const counts = anomalyCounts();
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return counts.critical > 0 || counts.high > 0;
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});
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const hasMildAnomalies = createMemo(() => {
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const counts = anomalyCounts();
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return !hasAnomalies() && (counts.medium > 0 || counts.low > 0);
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});
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const totalFindings = createMemo(() => {
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const s = status();
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if (!s) return 0;
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@ -48,32 +74,48 @@ export function AIStatusIndicator() {
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});
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const tooltipText = createMemo(() => {
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const s = status();
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if (!s) return 'AI Patrol status unavailable';
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if (!s.enabled) return 'AI Patrol disabled';
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if (s.license_required) {
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if (s.license_status === 'active') {
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return 'AI Patrol is not included in this license tier';
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}
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if (s.license_status === 'expired') {
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return 'AI Patrol license expired - upgrade to restore';
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}
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return 'AI Patrol requires Pulse Pro';
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}
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if (!s.running) return 'AI Patrol not running';
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const parts: string[] = [];
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if (s.summary.critical > 0) parts.push(`${s.summary.critical} critical`);
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if (s.summary.warning > 0) parts.push(`${s.summary.warning} warning`);
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if (s.summary.watch > 0) parts.push(`${s.summary.watch} watch`);
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if (parts.length === 0) return 'AI: All systems healthy';
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return `AI: ${parts.join(', ')}`;
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// Patrol status
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const s = status();
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if (s?.enabled && s?.running) {
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if (s.summary.critical > 0) parts.push(`${s.summary.critical} critical findings`);
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if (s.summary.warning > 0) parts.push(`${s.summary.warning} warnings`);
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if (s.summary.watch > 0) parts.push(`${s.summary.watch} watching`);
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}
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// Anomaly status
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const counts = anomalyCounts();
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const anomalyTotal = totalAnomalies();
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if (anomalyTotal > 0) {
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const anomalyParts: string[] = [];
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if (counts.critical > 0) anomalyParts.push(`${counts.critical} critical`);
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if (counts.high > 0) anomalyParts.push(`${counts.high} high`);
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if (counts.medium > 0) anomalyParts.push(`${counts.medium} medium`);
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if (counts.low > 0) anomalyParts.push(`${counts.low} low`);
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parts.push(`Anomalies: ${anomalyParts.join(', ')}`);
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}
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if (parts.length === 0) {
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if (!s?.enabled) return 'AI Patrol disabled';
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if (s?.license_required) {
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if (s.license_status === 'active') {
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return 'AI Patrol is not included in this license tier';
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}
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if (s.license_status === 'expired') {
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return 'AI Patrol license expired';
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}
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return 'AI Patrol requires Pulse Pro';
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}
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return 'AI: All systems healthy';
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}
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return `AI Intelligence: ${parts.join(' | ')}`;
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});
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const statusClass = createMemo(() => {
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if (hasIssues()) return 'ai-status--issues';
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if (hasWatch()) return 'ai-status--watch';
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if (hasIssues() || hasAnomalies()) return 'ai-status--issues';
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if (hasWatch() || hasMildAnomalies()) return 'ai-status--watch';
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return 'ai-status--healthy';
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});
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@ -82,16 +124,25 @@ export function AIStatusIndicator() {
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navigate('/alerts?subtab=ai-insights');
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};
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// Combined total for badge
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const badgeCount = createMemo(() => totalFindings() + totalAnomalies());
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// Show indicator if patrol is enabled OR if we have anomalies (anomalies work without patrol)
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const showIndicator = createMemo(() => {
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const s = status();
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return s?.enabled || totalAnomalies() > 0;
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});
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return (
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<Show when={status()?.enabled}>
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<Show when={showIndicator()}>
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<button
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class={`ai-status-indicator ${statusClass()}`}
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onClick={handleClick}
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title={tooltipText()}
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>
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<span class="ai-status-icon">
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<Show when={hasIssues()} fallback={
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<Show when={hasWatch()} fallback={
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<Show when={hasIssues() || hasAnomalies()} fallback={
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<Show when={hasWatch() || hasMildAnomalies()} fallback={
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<svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
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<path d="M12 22c5.523 0 10-4.477 10-10S17.523 2 12 2 2 6.477 2 12s4.477 10 10 10z" />
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<path d="M9 12l2 2 4-4" />
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@ -112,8 +163,8 @@ export function AIStatusIndicator() {
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</svg>
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</Show>
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</span>
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<Show when={totalFindings() > 0}>
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<span class="ai-status-count">{totalFindings()}</span>
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<Show when={badgeCount() > 0}>
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<span class="ai-status-count">{badgeCount()}</span>
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</Show>
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</button>
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</Show>
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@ -121,3 +172,4 @@ export function AIStatusIndicator() {
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}
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export default AIStatusIndicator;
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@ -396,6 +396,179 @@ func (s *Store) GetAllAnomalies(metricsProvider func(resourceID string) map[stri
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return allAnomalies
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}
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// TrendPrediction represents a forecast for when a resource might be exhausted
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type TrendPrediction struct {
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ResourceID string `json:"resource_id"`
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ResourceName string `json:"resource_name,omitempty"`
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ResourceType string `json:"resource_type,omitempty"`
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Metric string `json:"metric"`
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CurrentValue float64 `json:"current_value"` // Current % usage
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DailyChange float64 `json:"daily_change"` // Average change per day
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DaysToFull int `json:"days_to_full"` // Estimated days until 100% (or -1 if decreasing/stable)
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Severity string `json:"severity"` // "critical", "warning", "info"
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Description string `json:"description"`
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ConfidenceNote string `json:"confidence_note,omitempty"`
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}
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// CalculateTrend analyzes a time series of values and predicts future exhaustion
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// samples should be ordered oldest to newest, with at least 2 days of data
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// currentValue is the current percentage usage (0-100)
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// capacity represents 100% (for percentage-based predictions)
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func CalculateTrend(samples []float64, currentValue float64) *TrendPrediction {
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if len(samples) < 5 {
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return nil // Not enough data for meaningful trend
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}
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// Simple linear regression to find slope
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n := float64(len(samples))
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// Calculate means
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sumX := 0.0
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sumY := 0.0
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for i, v := range samples {
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sumX += float64(i)
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sumY += v
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}
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meanX := sumX / n
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meanY := sumY / n
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// Calculate slope (least squares)
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numerator := 0.0
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denominator := 0.0
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for i, v := range samples {
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x := float64(i)
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numerator += (x - meanX) * (v - meanY)
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denominator += (x - meanX) * (x - meanX)
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}
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if denominator == 0 {
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return nil // Can't calculate slope
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}
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slope := numerator / denominator
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// slope is change per sample, convert to daily change
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// Assume samples are taken regularly; if 24 samples per day, divide by 24
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// For now, assume hourly samples = 24 per day
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samplesPerDay := 24.0
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dailyChange := slope * samplesPerDay
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prediction := &TrendPrediction{
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CurrentValue: currentValue,
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DailyChange: dailyChange,
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}
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// Calculate days to full if trending upward
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if dailyChange > 0.1 { // More than 0.1% increase per day
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remaining := 100.0 - currentValue
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if remaining > 0 {
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daysToFull := remaining / dailyChange
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prediction.DaysToFull = int(math.Ceil(daysToFull))
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// Set severity based on time to full
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if prediction.DaysToFull <= 7 {
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prediction.Severity = "critical"
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prediction.Description = formatTrendDescription(prediction.DaysToFull, dailyChange, "critical")
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} else if prediction.DaysToFull <= 30 {
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prediction.Severity = "warning"
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prediction.Description = formatTrendDescription(prediction.DaysToFull, dailyChange, "warning")
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} else {
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prediction.Severity = "info"
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prediction.Description = formatTrendDescription(prediction.DaysToFull, dailyChange, "info")
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}
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} else {
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prediction.DaysToFull = 0
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prediction.Severity = "critical"
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prediction.Description = "Resource at capacity"
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}
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} else if dailyChange < -0.1 {
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// Decreasing trend
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prediction.DaysToFull = -1
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prediction.Severity = "info"
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daysToEmpty := currentValue / (-dailyChange)
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prediction.Description = "Usage declining - at current rate, will reach 0% in " + formatDays(int(math.Ceil(daysToEmpty)))
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} else {
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// Stable
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prediction.DaysToFull = -1
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prediction.Severity = "info"
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prediction.Description = "Usage stable - no significant trend detected"
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}
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return prediction
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}
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// formatTrendDescription creates human-readable trend descriptions
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func formatTrendDescription(daysToFull int, dailyChange float64, severity string) string {
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timeFrame := formatDays(daysToFull)
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changeDesc := ""
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if dailyChange >= 1 {
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changeDesc = " (+" + floatToStr(dailyChange, 1) + "% per day)"
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} else {
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changeDesc = " (+" + floatToStr(dailyChange, 2) + "% per day)"
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}
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switch severity {
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case "critical":
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return "⚠️ Resource will be full in " + timeFrame + changeDesc
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case "warning":
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return "Resource approaching capacity - full in " + timeFrame + changeDesc
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default:
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return "Trending toward full in " + timeFrame + changeDesc
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}
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}
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// formatDays converts days to human readable format
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func formatDays(days int) string {
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if days <= 0 {
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return "now"
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}
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if days == 1 {
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return "1 day"
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}
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if days < 7 {
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return string([]byte{'0' + byte(days)}) + " days"
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}
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if days < 14 {
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return "~1 week"
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}
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if days < 30 {
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weeks := days / 7
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return "~" + string([]byte{'0' + byte(weeks)}) + " weeks"
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}
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months := days / 30
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if months == 1 {
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return "~1 month"
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}
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if months < 12 {
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return "~" + string([]byte{'0' + byte(months)}) + " months"
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}
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return ">1 year"
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}
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// floatToStr converts float to string with given precision
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func floatToStr(f float64, precision int) string {
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// Simple implementation for small numbers
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intPart := int(f)
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fracPart := f - float64(intPart)
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if precision == 1 {
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fracPart = math.Round(fracPart*10) / 10
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if fracPart < 0.1 {
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return string([]byte{'0' + byte(intPart)})
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}
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d := byte('0' + int(fracPart*10))
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return string([]byte{'0' + byte(intPart), '.', d})
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}
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fracPart = math.Round(fracPart*100) / 100
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if fracPart < 0.01 {
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return string([]byte{'0' + byte(intPart)})
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}
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d1 := byte('0' + int(fracPart*10))
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d2 := byte('0' + int(fracPart*100)%10)
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return string([]byte{'0' + byte(intPart), '.', d1, d2})
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}
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// ResourceCount returns the number of resources with baselines
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func (s *Store) ResourceCount() int {
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s.mu.RLock()
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@ -403,6 +576,7 @@ func (s *Store) ResourceCount() int {
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return len(s.baselines)
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}
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// FlatBaseline is a flattened representation of a single metric baseline for API responses
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type FlatBaseline struct {
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ResourceID string `json:"resource_id"`
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