{"id":618,"date":"2026-06-09T05:00:17","date_gmt":"2026-06-09T05:00:17","guid":{"rendered":"https:\/\/struct.ai\/articles\/cloud-monitoring-alerting-best-practices\/"},"modified":"2026-09-04T05:02:46","modified_gmt":"2026-09-04T05:02:46","slug":"cloud-monitoring-alerting-best-practices","status":"publish","type":"post","link":"https:\/\/struct.ai\/articles\/cloud-monitoring-alerting-best-practices\/","title":{"rendered":"Best Practices for Cloud Monitoring Alerting in 2026"},"content":{"rendered":"<p><em>Written by: Nimesh Chakravarthi, Co-founder &amp; CTO, Struct | Last updated: August 25, 2026<\/em><\/p>\n<h2>Key Takeaways<\/h2>\n<ul>\n<li>\n<p>Redesign alerts around user impact and SLO burn rates, routing only P1 events to human pages while automating investigation and resolution verification for everything else.<\/p>\n<\/li>\n<li>\n<p>Apply a strict three-tier severity model (P1 Page, P2 Ticket, Info Record) and require every P1 alert to meet ten explicit criteria before it can page an engineer.<\/p>\n<\/li>\n<li>\n<p>Use multi-window burn-rate rules (14.4\u00d7 fast-burn for pages, 6\u00d7 slow-burn for tickets) to cut non-actionable pages in half and keep alert noise ratio below 20\u201330%.<\/p>\n<\/li>\n<li>\n<p>Implement correlation before notification, automated first-pass investigation, and incident resolution verification to reduce triage time by up to 80% and reclaim dozens of engineer-hours monthly.<\/p>\n<\/li>\n<li>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/cal.com\/deepanm\/struct-demo\">Automate your on-call runbook with Struct <\/a>to encode institutional knowledge, expand on-call rotation safely, and let engineers sleep through the night while production stays healthy.<\/p>\n<\/li>\n<\/ul>\n<h2>2026 Best Practice 1: Alert Classification and the Three-Tier Model<\/h2>\n<p>Classify every alert into Page, Ticket, or Record so only real emergencies wake humans. Use the three-tier model and require every P1 to satisfy ten explicit criteria before it is permitted to page an engineer. Google recommends evaluating alert strategies across four dimensions, alert precision (significant events alerted \u00f7 total alerts), incident recall (significant incidents detected \u00f7 total significant incidents), detection time, and reset time, rather than optimizing solely for fewer notifications. These ten criteria turn that framework into a concrete gate for any alert that wants to reach the Page tier.<\/p>\n<p>Before any rule is allowed to page an engineer, verify it meets all ten of the following criteria:<\/p>\n<ol>\n<li>\n<p>Represents actual or imminent user impact<\/p>\n<\/li>\n<li>\n<p>Requires immediate human action that improves outcomes<\/p>\n<\/li>\n<li>\n<p>Cannot be resolved by automation alone<\/p>\n<\/li>\n<li>\n<p>Notifies the correct owning team<\/p>\n<\/li>\n<li>\n<p>Contains sufficient investigative context in the alert body<\/p>\n<\/li>\n<li>\n<p>Has a documented runbook or known remediation path<\/p>\n<\/li>\n<li>\n<p>Is not a duplicate of an already-open incident<\/p>\n<\/li>\n<li>\n<p>Has been validated against historical data to confirm low false-positive rate<\/p>\n<\/li>\n<li>\n<p>Fires on a symptom visible to users, not an internal infrastructure cause<\/p>\n<\/li>\n<li>\n<p>Has a defined escalation path if the on-call engineer cannot resolve it<\/p>\n<\/li>\n<\/ol>\n<p>Alerts that do not meet these criteria belong in the Ticket or Record tier, never the Page tier. Applying this filter alone removes most non-actionable pages in many engineering organizations and creates a cleaner foundation for the remaining practices.<\/p>\n<h2>2026 Best Practice 2: SLO-Driven Routing and Multi-Window Burn-Rate Rules<\/h2>\n<p>Use SLO burn rates to decide which issues deserve a page and which should open tickets. Implement fast-burn alerts at 14.4\u00d7 over paired 1-hour and 5-minute windows for pages, and slow-burn alerts at 6\u00d7 over paired 6-hour and 30-minute windows for tickets, per the <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/backendbytes.com\/articles\/sre-slos-slis-error-budgets\">Google SRE Workbook multi-window burn-rate framework<\/a>. This two-tier default filters out transient spikes while catching sustained error-budget consumption that single-window approaches miss.<\/p>\n<p><a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/devopsaitoolkit.com\/blog\/multi-window-burn-rate-alerts-for-slos-that-work\">For a 99.9% availability SLO over 30 days, a 14.4\u00d7 burn rate exhausts the full error budget in roughly two days, which makes it the correct threshold for immediate human escalation<\/a>. The 6\u00d7 slow-burn tier catches quieter degradation that would exhaust the budget over a week, serious enough for a ticket but not for a 3 a.m. page.<\/p>\n<p>The north-star metric for this system is <strong>actionable pages %<\/strong>, the share of pages that required immediate human action. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/devopsschool.com\/blog\/observability-engineering-manager-role-blueprint-responsibilities-skills-kpis-and-career-path\">Alert noise ratio, the percentage of non-actionable alerts out of total alerts, should stay below 20\u201330% in mature organizations and be tracked weekly as a core observability KPI<\/a>. Struct can track these metrics continuously so teams see the impact of SLO-driven routing in real time.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/cal.com\/deepanm\/struct-demo\"><strong>Track your actionable pages % in real time with Struct<\/strong><\/a> and see improvement within the first two weeks.<\/p>\n<h2>2026 Best Practice 3: SLO-Driven Routing and Pager Budgets in Practice<\/h2>\n<p>Turn SLO targets into concrete pager budgets that protect engineers from alert overload. Set a hard target of no more than 20% non-actionable alerts and require pages per on-call shift to trend downward quarter-over-quarter. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/devopsschool.com\/blog\/staff-observability-engineer-role-blueprint-responsibilities-skills-kpis-and-career-path\">Pages per on-call shift is a monthly KPI that measures alert volume paging the platform team, with a target of trending downward quarter-over-quarter to demonstrate improved alert tuning and platform stability<\/a>.<\/p>\n<p>Achieving that target requires a disciplined workflow for every new alert rule. Use this five-step process before any alert goes live:<\/p>\n<ol>\n<li>\n<p><strong>Define the SLI and SLO<\/strong>, identify the user-visible symptom and set the target reliability percentage.<\/p>\n<\/li>\n<li>\n<p><strong>Calculate burn-rate thresholds<\/strong>, use the formula F \u00d7 P \u00f7 W, where F is the budget fraction that justifies interrupting a human, P is the compliance period, and W is the window length.<\/p>\n<\/li>\n<li>\n<p><strong>Write recording rules first<\/strong>, <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/chroniclesofasre.substack.com\/p\/multi-window-burn-rate-alerting-the\">manually verify each recording rule&#8217;s output during both known-good and known-degraded periods before enabling any alerting rules that depend on them<\/a>.<\/p>\n<\/li>\n<li>\n<p><strong>Enable test mode<\/strong>, run the alert in shadow mode for one week, logging fires without paging anyone, to validate precision and recall.<\/p>\n<\/li>\n<li>\n<p><strong>Assign tier and route<\/strong>, P1 to PagerDuty, P2 to Linear or Jira, Info to log storage only.<\/p>\n<\/li>\n<\/ol>\n<p><a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/backendbytes.com\/articles\/sre-slos-slis-error-budgets\">When error budget remaining drops below 25%, freeze feature deployments and shift the team to reliability work, and at 0% remaining, only emergency fixes are permitted and the VP of Engineering is notified<\/a>. This policy connects alerting decisions directly to product and release management.<\/p>\n<h2>2026 Best Practice 4: Correlation Before Notification<\/h2>\n<p>Reduce alert volume before it reaches humans by correlating signals into single incidents. Deduplicate and correlate alerts at the infrastructure layer before any notification reaches an engineer. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/rootly.com\/alert-management\/alert-deduplication-and-correlation\">Alert correlation should combine service ownership, infrastructure dependencies from service catalogs or CMDBs, affected environments, regions, historical co-occurrence patterns, and deployment activity to group related alerts into a single incident<\/a>.<\/p>\n<p>Three techniques form the foundation of a production-grade correlation pipeline and work best in combination. Start with fingerprint deduplication, then add topology-aware grouping, and finally layer maintenance window suppression.<\/p>\n<ul>\n<li>\n<p><strong>Fingerprint deduplication<\/strong>, hash stable keys such as service name, environment, resource identifier, and alert condition to collapse repeated events into one active alert. Fingerprint deduplication is table stakes that should be configured before any advanced correlation method.<\/p>\n<\/li>\n<li>\n<p><strong>Topology-aware grouping<\/strong>, map service dependencies so that a database outage producing separate connection-failure, timeout, latency, and health-check alerts surfaces as one incident, not four pages. Correlation engines can achieve significant alert-to-incident compression.<\/p>\n<\/li>\n<li>\n<p><strong>Maintenance window suppression<\/strong>, mute notifications for scheduled periods while preserving event logs for forensic review. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/dataendure.com\/blog\/a-practical-guide-to-reducing-alert-noise\">Suppression during maintenance windows avoids floods of alerts like 500 &#8220;Server Unreachable&#8221; messages while still logging events for audit<\/a>. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/systemshardening.com\/articles\/observability\/alert-correlation\">Suppression lists should be defined in version-controlled YAML with explicit expiration dates and require quarterly review to prevent permanent suppression of real signals<\/a>.<\/p>\n<\/li>\n<\/ul>\n<p>The <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/docs.aws.amazon.com\/wellarchitected\/latest\/framework\/welcome.html\">AWS Well-Architected Framework<\/a> recommends alerting on symptoms rather than causes and grouping related signals before escalation, a principle that topology-aware correlation implements directly.<\/p>\n<h2>2026 Best Practice 5: Automated First-Pass Investigation with Struct<\/h2>\n<p>Cut triage time by automating the first investigation step for every alert. Correlation reduces the number of alerts that reach an engineer, but each remaining alert still requires manual investigation. Automated first-pass investigation removes that manual work entirely and becomes the single highest-leverage intervention for reducing mean time to resolution.<\/p>\n<p>Struct integrates directly into Slack and PagerDuty, intercepts alerts the moment they fire, and delivers a complete root cause analysis, including correlated logs, unified timeline, blast radius, and suggested fixes, within 5 to 10 minutes before an engineer opens their laptop. The impact is measurable: customer Arcana cut investigation time from 30 minutes to 2 minutes, reclaiming 56 engineer-hours per month and running more than 2,100 automated investigations monthly. A Series A fintech with over 40 engineers reduced triage time by 80% and protected strict SLAs after a 10-minute Struct setup.<\/p>\n<p>Struct sits on top of existing observability tooling, such as Datadog, Grafana, Sentry, AWS CloudWatch, GCP Logs, Azure, and Prometheus, as a dedicated investigation layer. It does not replace those tools, it removes the manual work of querying them at 3 a.m. Engineers receive a dynamically generated dashboard with supporting evidence, relevant charts, and a unified timeline merging events across the full stack. A Slack-native conversational interface lets engineers ask follow-up questions, test hypotheses, or pull additional logs without leaving their communication hub.<\/p>\n<p>Struct&#8217;s Deploy Guard feature extends this capability upstream. It reviews instrumentation on pull requests, suggests alerts, and runs post-deploy health checks so alerting quality improves before incidents happen.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/cal.com\/deepanm\/struct-demo\"><strong>See Struct run your next on-call investigation automatically<\/strong><\/a> and compare triage time before and after.<\/p>\n<h2>2026 Best Practice 6: Incident Resolution Verification<\/h2>\n<p>Confirm that incidents are truly resolved by checking live data instead of relying only on human judgment. Incident resolution verification, automatically confirming that an incident is resolved by checking observability data, closes the loop that manual on-call processes leave open. Struct&#8217;s Incident Tracker runs an approximately 1-minute automated verification loop against live observability data to confirm an incident is actually resolved before marking it closed.<\/p>\n<p>Without automated verification, engineers frequently close incidents prematurely under time pressure, then get paged again minutes later when the condition re-fires. The 1-minute verification loop removes this pattern by requiring the observability data itself, not human assessment, to confirm recovery.<\/p>\n<p>The combined workflow of automated first-pass investigation plus incident resolution verification reduces the total human action requirement to two decision points. Engineers review Struct&#8217;s root cause summary and confirm the verified resolution. Everything between those two points, including log correlation, timeline construction, hypothesis testing, and recovery confirmation, runs without human involvement. This architecture makes 80% triage-time reduction achievable in practice, not just in theory.<\/p>\n<h2>2026 Best Practice 7: Runbook-Template Checklist and Monthly Review Cadence<\/h2>\n<p>Standardize responses with runbooks so any engineer can handle P1 alerts confidently. Automated investigation handles the majority of incidents, but the remaining cases that require human judgment still need clear remediation paths. Every alert that reaches P1 status must have a corresponding runbook.<\/p>\n<p>Struct accepts custom runbooks directly, encoding your senior engineers&#8217; institutional knowledge so junior engineers receive a contextualized, step-by-step starting point for every alert. This approach makes it safe to expand on-call rotation without requiring deep system expertise from every participant.<\/p>\n<p>A production-ready runbook template includes:<\/p>\n<ul>\n<li>\n<p>Affected service and owning team<\/p>\n<\/li>\n<li>\n<p>SLO or user symptom being violated<\/p>\n<\/li>\n<li>\n<p>Start time and current severity<\/p>\n<\/li>\n<li>\n<p>Recent deploys and configuration changes<\/p>\n<\/li>\n<li>\n<p>Top traces, logs, events, and dashboard links<\/p>\n<\/li>\n<li>\n<p>Known remediation steps and escalation path<\/p>\n<\/li>\n<li>\n<p>Verification criteria confirming resolution<\/p>\n<\/li>\n<\/ul>\n<p>Run a monthly alert review during the first quarter using these <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/dora.dev\/research\/\">DORA-aligned<\/a> metrics: CRITICAL false-positive rate, time-to-acknowledgement, budget-exhaustion events without a prior alert, and actionable pages % trend. <a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/chroniclesofasre.substack.com\/p\/multi-window-burn-rate-alerting-the\">Start with only the CRITICAL and HIGH burn-rate tiers, and deploy MEDIUM and LOW tiers only after the first two tiers have remained stable for two weeks so that calibration remains tractable<\/a>. Automate your on-call runbook with Struct and keep these reviews lightweight.<\/p>\n<p><a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/cal.com\/deepanm\/struct-demo\"><strong>Turn your runbooks into automated on-call workflows with Struct<\/strong><\/a> and support a broader on-call rotation safely.<\/p>\n<h2>Metrics Dashboard: Tracking Actionable Pages %<\/h2>\n<p>Track a small set of metrics weekly to see whether these practices are working. Together, these five metrics show alert quality, operational burden, and investigation efficiency in a single view.<\/p>\n<table style=\"min-width: 100px\">\n<colgroup>\n<col style=\"min-width: 25px\">\n<col style=\"min-width: 25px\">\n<col style=\"min-width: 25px\">\n<col style=\"min-width: 25px\"><\/colgroup>\n<tbody>\n<tr>\n<th colspan=\"1\" rowspan=\"1\">\n<p>Metric<\/p>\n<\/th>\n<th colspan=\"1\" rowspan=\"1\">\n<p>Definition<\/p>\n<\/th>\n<th colspan=\"1\" rowspan=\"1\">\n<p>Target<\/p>\n<\/th>\n<th colspan=\"1\" rowspan=\"1\">\n<p>Source<\/p>\n<\/th>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Actionable pages %<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Pages requiring immediate human action \u00f7 total pages<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u2265 80%<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Google SRE alert precision framework<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Alert noise ratio<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Non-actionable alerts \u00f7 total alerts<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u2264 20%<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p><a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/devopsschool.com\/blog\/observability-engineering-manager-role-blueprint-responsibilities-skills-kpis-and-career-path\">Observability Engineering Manager KPI framework<\/a><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Pages per on-call shift<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Total pages received per engineer per shift<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Trending downward QoQ<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p><a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/devopsschool.com\/blog\/staff-observability-engineer-role-blueprint-responsibilities-skills-kpis-and-career-path\">Staff Observability Engineer KPI framework<\/a><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Mean triage time<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Time from alert fire to root cause identified<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u2264 10 minutes with automation<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Struct customer data<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Operational toil share<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>% of engineer week spent on reactive ops work<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>\u2264 15%<\/p>\n<\/td>\n<td colspan=\"1\" rowspan=\"1\">\n<p>Industry reports<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What minimum tooling maturity does a team need before implementing these practices?<\/h3>\n<p>Teams need basic logging, trace IDs, and at least one alerting trigger source already in place. The ideal starting point is a team already using Sentry or Datadog for exceptions, AWS CloudWatch or GCP Logs for infrastructure telemetry, and Slack or PagerDuty for alert delivery. Without those foundations, SLO-driven routing and automated investigation cannot function reliably because the underlying observability data does not exist.<\/p>\n<h3>How does Struct integrate with an existing observability stack?<\/h3>\n<p>Struct sits on top of existing tools as a dedicated investigation layer, and it does not replace Datadog, Grafana, Sentry, or any other observability platform. Setup takes under 10 minutes. The integration process requires three steps: authenticate your issue source such as Slack or PagerDuty, connect your code repository such as GitHub, and link your observability context such as Datadog, CloudWatch, or GCP Logs. Once connected, Struct automatically investigates every alert that fires in your configured channels and delivers root cause analysis and suggested fixes before an engineer needs to intervene.<\/p>\n<h3>Is Struct compliant with SOC 2 and HIPAA requirements?<\/h3>\n<p>Struct is fully SOC 2 Type II and HIPAA compliant, with the full compliance posture documented at trust.struct.ai. Logs are accessed and processed ephemerally. For the vast majority of Series A\u2013C B2B SaaS companies, this compliance standard covers all requirements. Teams with strict enterprise rules requiring full on-premise deployment and zero log egress from their VPC should evaluate Struct&#8217;s Enterprise tier, which includes sidecar and on-prem support options.<\/p>\n<h3>How quickly can a junior engineer become effective on on-call rotation with Struct?<\/h3>\n<p>Struct acts as an automated senior engineer for the first pass of every incident, digesting your team&#8217;s custom runbooks and encoding institutional knowledge into every investigation output. A junior engineer receives a fully contextualized starting point, including blast radius, root cause hypothesis, and suggested remediation steps, for every alert without needing deep systemic knowledge of the entire stack. This approach makes it safe to expand on-call rotation to newer engineers immediately after setup, rather than waiting months for them to accumulate tribal knowledge.<\/p>\n<h3>What happens during the 30-day risk-free pilot?<\/h3>\n<p>The pilot includes white-glove onboarding, full access to Struct&#8217;s automated investigation capabilities, and integration with your existing alerting channels. Your team runs real on-call investigations through Struct for 30 days, with no engineering time required for setup beyond the initial integration. At the end of the pilot, you have concrete data on triage time reduction, actionable pages %, and engineer-hours reclaimed, the same categories Arcana used to validate their results.<\/p>\n<h2>Conclusion and Next Steps<\/h2>\n<p>Eliminating on-call fatigue over 90 days works best as a staged rollout. In the first 30 days, implement the three-tier severity model, deploy multi-window burn-rate alerts for your top five SLOs, and establish baseline metrics for actionable pages % and pages per shift. In days 31\u201360, add topology-aware correlation and maintenance window suppression, and run Struct&#8217;s automated first-pass investigation across all configured alert channels. In days 61\u201390, activate incident resolution verification through Struct&#8217;s Incident Tracker, complete the first monthly alert review, and expand on-call rotation to junior engineers using Struct-generated runbook outputs.<\/p>\n<p><a target=\"_blank\" rel=\"noindex nofollow\" href=\"https:\/\/grepr.ai\/blog\/alert-fatigue-what-it-costs-engineering-teams-in-2026\">Eighty-three percent of engineers admit to ignoring or dismissing alerts at least occasionally, and 44% of organizations had an outage in the past year tied directly to a suppressed or ignored alert<\/a>. The practices in this guide, SLO-driven routing, correlation before notification, automated investigation, and incident resolution verification, provide structural interventions that break that pattern.<\/p>\n<p>Struct implements the investigation and verification layers on top of your existing observability stack, with a 30-day risk-free pilot to validate results against your own production data. <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https:\/\/cal.com\/deepanm\/struct-demo\"><strong>Measure your reduction in triage time and pages per shift with a Struct pilot<\/strong><\/a> and see the impact within the first two weeks.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cut on-call fatigue with 7 proven alerting best practices. Struct automates triage so your team pages less and resolves faster. Start free.<\/p>\n","protected":false},"author":73,"featured_media":905,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-618","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/posts\/618","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/comments?post=618"}],"version-history":[{"count":1,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/posts\/618\/revisions"}],"predecessor-version":[{"id":907,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/posts\/618\/revisions\/907"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/media\/905"}],"wp:attachment":[{"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/media?parent=618"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/categories?post=618"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/struct.ai\/articles\/wp-json\/wp\/v2\/tags?post=618"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}