{"id":2186,"date":"2026-01-28T13:55:53","date_gmt":"2026-01-28T13:55:53","guid":{"rendered":"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/"},"modified":"2026-01-28T13:55:53","modified_gmt":"2026-01-28T13:55:53","slug":"debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost","status":"publish","type":"post","link":"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/","title":{"rendered":"Debug Multi-Step Agents Faster: Agent Observability With Tracing, Evals, And Cost","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 ez-toc-wrap-center counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ffffff;color:#ffffff\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ffffff;color:#ffffff\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#A_late-night_incident_that_couldve_been_a_5-minute_fix\" >A late-night incident that could\u2019ve been a 5-minute fix<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#What_%E2%80%9Cagent_observability%E2%80%9D_means_in_2025\" >What \u201cagent observability\u201d means in 2025<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#The_7_proven_checks_before_you_launch\" >The 7 proven checks before you launch<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#1_Trace_every_step_not_just_the_final_answer\" >1) Trace every step, not just the final answer<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#2_Record_%E2%80%9Cwhy%E2%80%9D_signals_but_keep_it_lightweight\" >2) Record \u201cwhy\u201d signals, but keep it lightweight<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#3_Add_online_eval_hooks_to_catch_drift\" >3) Add online eval hooks to catch drift<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#4_Track_cost_per_successful_task_not_cost_per_call\" >4) Track cost per successful task, not cost per call<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#5_Break_down_latency_like_a_detective\" >5) Break down latency like a detective<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#6_Instrument_with_OpenTelemetry_where_it_fits\" >6) Instrument with OpenTelemetry where it fits<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#7_Build_dashboards_that_answer_%E2%80%9Cwhat_broke%E2%80%9D_fast\" >7) Build dashboards that answer \u201cwhat broke\u201d fast<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#Common_mistakes_that_make_observability_painful\" >Common mistakes that make observability painful<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#Risks_what_can_go_wrong_if_you_%E2%80%9Cobserve_everything%E2%80%9D\" >Risks: what can go wrong if you \u201cobserve everything\u201d<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#A_simple_checklist_you_can_apply_this_week\" >A simple checklist you can apply this week<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#Tooling_options_open_source_vs_enterprise\" >Tooling options: open source vs enterprise<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#What_to_do_next\" >What to do next<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/debug-multi-step-agents-faster-agent-observability-with-tracing-evals-and-cost\/#Further_reading\" >Further reading<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"A_late-night_incident_that_couldve_been_a_5-minute_fix\"><\/span>A late-night incident that could\u2019ve been a 5-minute fix<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You ship a shiny new support agent on Friday. By Monday, a Slack thread is on fire: \u201cIt keeps looping,\u201d \u201cIt\u2019s slow,\u201d and \u201cWhy did it call the billing tool 19 times?\u201d Nobody can answer the simplest question: what actually happened, step by step.<\/p>\n<p>That\u2019s why agent observability matters. Agent observability is the difference between guessing and knowing when an AI agent fails in production.<\/p>\n<p><strong>In this article you\u2019ll learn&#8230;<\/strong><\/p>\n<ul>\n<li>What to capture in every agent run so debugging is fast.<\/li>\n<li>Which metrics expose reliability and cost issues early.<\/li>\n<li>How to add tracing, evaluation hooks, and guardrails without slowing your team down.<\/li>\n<li>A practical checklist you can apply this week.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"What_%E2%80%9Cagent_observability%E2%80%9D_means_in_2025\"><\/span>What \u201cagent observability\u201d means in 2025<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Agents are not single model calls. They are multi-step workflows with tools, retrieval, and memory. So, traditional app monitoring only shows you the symptoms. You also need the story of the run.<\/p>\n<p>As Maxim AI puts it, \u201cAI observability provides end-to-end visibility into agent behavior, spanning prompts, tool calls, retrievals, and multi-turn sessions.\u201d That scope is what makes observability feel new.<\/p>\n<p>In practice, good observability ties together:<\/p>\n<ul>\n<li>Session-level traces with a single trace ID per user task.<\/li>\n<li>Structured logs for prompts, tool calls, and key decisions.<\/li>\n<li>Evaluation signals, both offline tests and online sampling.<\/li>\n<li>Cost and latency breakdowns by step.<\/li>\n<li>Governance basics like redaction and access control.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"The_7_proven_checks_before_you_launch\"><\/span>The 7 proven checks before you launch<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This is the minimum playbook for teams shipping tool-using agents. Think of it like checking the oil, brakes, and lights before a road trip.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"1_Trace_every_step_not_just_the_final_answer\"><\/span>1) Trace every step, not just the final answer<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>If you only log the final response, you miss the failure. Instead, capture a timeline:<\/p>\n<ul>\n<li>User input and normalized intent.<\/li>\n<li>Prompt template version and system instructions.<\/li>\n<li>Model name, parameters, and token counts.<\/li>\n<li>Tool calls, inputs, outputs, and errors.<\/li>\n<li>Retrieval queries and which documents were used.<\/li>\n<li>Final output plus any post-processing.<\/li>\n<\/ul>\n<p>Also, add a correlation ID that follows the request across services. Then, when a tool times out, you can see the chain reaction.<\/p>\n<p><strong>Mini case:<\/strong> A revops team shipped a lead research agent. It looked fine in demos. In production, it stalled on 8% of accounts because one vendor API returned a slow 429 retry cycle. Tracing made the culprit obvious within an hour.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"2_Record_%E2%80%9Cwhy%E2%80%9D_signals_but_keep_it_lightweight\"><\/span>2) Record \u201cwhy\u201d signals, but keep it lightweight<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Teams often try to log raw chain-of-thought. That\u2019s risky and usually unnecessary.<\/p>\n<p>Instead, log lightweight reasoning artifacts:<\/p>\n<ul>\n<li>The plan or step list the agent produced.<\/li>\n<li>Which tool it selected and a short reason label.<\/li>\n<li>Confidence flags like \u201clow evidence\u201d or \u201cmissing fields.\u201d<\/li>\n<\/ul>\n<p>This gives you the why without storing sensitive internal reasoning. Moreover, it is easier to redact.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"3_Add_online_eval_hooks_to_catch_drift\"><\/span>3) Add online eval hooks to catch drift<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Offline test sets are essential. However, they miss real user weirdness.<\/p>\n<p>So, sample production runs and score them. You can start simple:<\/p>\n<ol>\n<li>Define 20 to 50 \u201cmust-pass\u201d tasks.<\/li>\n<li>Run them on every prompt or model change.<\/li>\n<li>In production, score 1% to 5% of sessions.<\/li>\n<li>Alert on regressions in pass rate.<\/li>\n<\/ol>\n<p>Arize sums it up bluntly: \u201cBuilding proof of concepts is easy; engineering highly functional agents is not.\u201d Evals are how you stay on the hard side of that sentence.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"4_Track_cost_per_successful_task_not_cost_per_call\"><\/span>4) Track cost per successful task, not cost per call<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Agents hide costs in loops. A single user request can trigger multiple model calls plus tools.<\/p>\n<p>Therefore, your core cost metric should be <strong>cost per successful task<\/strong>. Support it with:<\/p>\n<ul>\n<li>Tokens per session.<\/li>\n<li>Tool calls per session.<\/li>\n<li>Retries per tool.<\/li>\n<li>Cost by step, including retrieval and external APIs.<\/li>\n<\/ul>\n<p><strong>Mini case:<\/strong> One team saw stable token use. Yet, their bill doubled. Traces showed a sneaky tool loop that hit a paid enrichment API repeatedly after a schema change.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"5_Break_down_latency_like_a_detective\"><\/span>5) Break down latency like a detective<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Users experience the slowest step. So, measure model latency, retrieval latency, tool latency, queue time, and total time to first token.<\/p>\n<p>Then, set budgets. For example, you might target 4 seconds for \u201csimple\u201d tasks and 12 seconds for \u201cdeep research\u201d tasks.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"6_Instrument_with_OpenTelemetry_where_it_fits\"><\/span>6) Instrument with OpenTelemetry where it fits<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>If you already use OpenTelemetry, connect agent traces to the rest of your stack. That\u2019s a big win for incident response.<\/p>\n<p>Start with a minimal span model:<\/p>\n<ul>\n<li>One root span per user session.<\/li>\n<li>Child spans for each model call.<\/li>\n<li>Child spans for each tool call.<\/li>\n<li>Attributes for prompt version, model, and tenant.<\/li>\n<\/ul>\n<p>Later, add baggage for user tier or feature flags. Overall, the goal is to make traces queryable and comparable.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"7_Build_dashboards_that_answer_%E2%80%9Cwhat_broke%E2%80%9D_fast\"><\/span>7) Build dashboards that answer \u201cwhat broke\u201d fast<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Dashboards should not be art projects. They should reduce time to resolution.<\/p>\n<p>A practical set:<\/p>\n<ul>\n<li>Task success rate and failure rate.<\/li>\n<li>Tool error rate by tool name.<\/li>\n<li>Loop rate where steps exceed a threshold.<\/li>\n<li>Cost per successful task by workflow.<\/li>\n<li>P95 latency by workflow and by step.<\/li>\n<\/ul>\n<p>If you have multi-agent routing, add handoff metrics too. Otherwise, you will blame the wrong agent.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Common_mistakes_that_make_observability_painful\"><\/span>Common mistakes that make observability painful<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Teams fall into the same traps. Fortunately, they are fixable.<\/p>\n<ul>\n<li>Logging only the final answer, then wondering why bugs are mysterious.<\/li>\n<li>Capturing sensitive data without redaction, then freezing the program.<\/li>\n<li>Mixing environments so staging and prod traces look identical.<\/li>\n<li>Using dashboards with no action thresholds.<\/li>\n<li>Shipping prompt changes without eval gates.<\/li>\n<li>Storing raw thoughts or internal instructions unnecessarily.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Risks_what_can_go_wrong_if_you_%E2%80%9Cobserve_everything%E2%80%9D\"><\/span>Risks: what can go wrong if you \u201cobserve everything\u201d<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Observability has sharp edges. If you ignore them, you create new incidents.<\/p>\n<ul>\n<li><strong>Privacy risk:<\/strong> Prompts and retrieved docs may contain PII. Redaction and access controls are mandatory.<\/li>\n<li><strong>Security risk:<\/strong> Tool inputs can include secrets. Mask API keys and tokens at ingestion.<\/li>\n<li><strong>Cost risk:<\/strong> Full-fidelity logging can be expensive at scale. Sample, compress, and keep retention short.<\/li>\n<li><strong>Compliance risk:<\/strong> Regulated industries may require data residency and audit trails.<\/li>\n<\/ul>\n<p>If you are unsure, start with minimal capture and expand. In short, don\u2019t turn your logs into a data leak.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"A_simple_checklist_you_can_apply_this_week\"><\/span>A simple checklist you can apply this week<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>Try this<\/strong> when you instrument your next workflow:<\/p>\n<ul>\n<li>Assign a trace ID to every user request.<\/li>\n<li>Log prompt template version and model version.<\/li>\n<li>Capture tool calls with inputs, outputs, and error codes.<\/li>\n<li>Store retrieval queries and top documents, with redaction.<\/li>\n<li>Compute cost per session and cost per successful task.<\/li>\n<li>Track P95 latency and loop rate.<\/li>\n<li>Add a small eval set and run it on every change.<\/li>\n<li>Define three alerts tied to user impact.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.agentixlabs.com\/\">Internal link: Agent observability playbooks<\/a><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Tooling_options_open_source_vs_enterprise\"><\/span>Tooling options: open source vs enterprise<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The market is splitting. Open source tools help you iterate quickly. Enterprise tools often win on governance and scale.<\/p>\n<p>If you are evaluating platforms, these comparisons help:<\/p>\n<p><a href=\"https:\/\/www.getmaxim.ai\/articles\/top-5-tools-for-ai-agent-observability-in-2025\/\">Maxim\u2019s tool roundup<\/a>.<\/p>\n<p><a href=\"https:\/\/arize.com\/llm-evaluation-platforms-top-frameworks\/\">Arize on evaluation<\/a>.<\/p>\n<p>One note on niche searches: if you see \u201cobserveit agent\u201d in your analytics, treat it as a sign people are hunting for monitoring answers, not a specific standard.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_to_do_next\"><\/span>What to do next<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You don\u2019t need perfection to start. You need a minimum viable observability layer.<\/p>\n<ol>\n<li>Pick one high-value workflow and instrument it end to end.<\/li>\n<li>Decide what data you must redact and who can view traces.<\/li>\n<li>Add cost and latency budgets, then alert on breaches.<\/li>\n<li>Create a 30-case eval set and wire it into CI.<\/li>\n<li>Review traces weekly and kill the top two failure modes.<\/li>\n<\/ol>\n<p>After two weeks, you will usually see fewer loops and faster debugging. Moreover, you will have real numbers for ROI.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>1) What is agent observability, in plain English?<\/strong><br \/>It is the ability to see what your agent did, step by step, so you can debug, measure, and improve it.<\/p>\n<p><strong>2) Do I need observability if my agent is \u201cjust prompts\u201d?<\/strong><br \/>Yes. Even simple agents can drift after model or prompt changes. Traces and evals prevent silent breakage.<\/p>\n<p><strong>3) What should I log first?<\/strong><br \/>Start with trace IDs, prompt versions, model metadata, tool calls, and errors. Then, add retrieval and cost.<\/p>\n<p><strong>4) How much traffic should I sample for online evals?<\/strong><br \/>Many teams start with 1% to 5%. Then, they increase sampling for high-risk workflows.<\/p>\n<p><strong>5) How do I avoid storing sensitive user data?<\/strong><br \/>Redact PII, mask secrets, and limit retention. Also, restrict trace access by role.<\/p>\n<p><strong>6) What metrics matter most for cost control?<\/strong><br \/>Cost per successful task, tokens per session, and tool calls per session. Also track retries and loop rate.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Further_reading\"><\/span>Further reading<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>LLM evaluation platforms and frameworks (Arize).<\/li>\n<li>AI agent observability tools in 2025 (Maxim AI).<\/li>\n<li>LLM observability platform comparisons (Agenta).<\/li>\n<li><a href=\"https:\/\/opentelemetry.io\/docs\/\">OpenTelemetry documentation<\/a> for distributed tracing.<\/li>\n<\/ul>\n<span class=\"et_bloom_bottom_trigger\"><\/span>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>A practical 2025-ready guide to agent observability: tracing, eval hooks, and cost and latency metrics that help you debug faster and ship reliable agents.<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":2185,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-2186","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-general"],"aioseo_notices":[],"gt_translate_keys":[{"key":"link","format":"url"}],"_links":{"self":[{"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/posts\/2186","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=2186"}],"version-history":[{"count":0,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/posts\/2186\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/media\/2185"}],"wp:attachment":[{"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=2186"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=2186"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=2186"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}