{"id":2204,"date":"2026-02-23T13:58:54","date_gmt":"2026-02-23T13:58:54","guid":{"rendered":"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/"},"modified":"2026-02-23T13:58:54","modified_gmt":"2026-02-23T13:58:54","slug":"agent-observability-for-tool-using-agents-stop-costly-loops","status":"publish","type":"post","link":"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/","title":{"rendered":"Agent observability for tool-using agents: stop costly loops","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\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Why_this_suddenly_feels_urgent\" >Why this suddenly feels urgent<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#In_this_article_youll_learn%E2%80%A6\" >In this article you\u2019ll learn\u2026<\/a><\/li><\/ul><\/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\/agent-observability-for-tool-using-agents-stop-costly-loops\/#What_agent_observability_includes_and_what_it_doesnt\" >What agent observability includes (and what it doesn\u2019t)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Trend_signals_why_teams_are_changing_their_monitoring_stack\" >Trend signals: why teams are changing their monitoring stack<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#The_minimal_viable_signal_set_MVSS_you_should_capture\" >The minimal viable signal set (MVSS) you should capture<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#A_simple_tracing_model_map_one_run_to_one_trace\" >A simple tracing model: map one run to one trace<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Two_real-world_failure_modes_and_the_signals_that_catch_them\" >Two real-world failure modes (and the signals that catch them)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Common_mistakes_teams_make_even_strong_teams\" >Common mistakes teams make (even strong teams)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Risks_how_observability_can_backfire\" >Risks: how observability can backfire<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#A_quick_decision_guide_what_to_instrument_first\" >A quick decision guide: what to instrument first<\/a><\/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\/agent-observability-for-tool-using-agents-stop-costly-loops\/#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-12\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Where_%E2%80%9Cobserveit_agent%E2%80%9D_fits_and_why_you_should_care\" >Where \u201cobserveit agent\u201d fits (and why you should care)<\/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\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Further_reading\" >Further reading<\/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\/agent-observability-for-tool-using-agents-stop-costly-loops\/#FAQ\" >FAQ<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Whats_the_difference_between_monitoring_and_observability_for_agents\" >What\u2019s the difference between monitoring and observability for agents?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Do_I_need_OpenTelemetry_to_start\" >Do I need OpenTelemetry to start?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#Whats_the_first_metric_worth_alerting_on\" >What\u2019s the first metric worth alerting on?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#How_do_I_measure_correctness_if_the_agent_is_non-deterministic\" >How do I measure correctness if the agent is non-deterministic?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#How_do_I_keep_traces_safe\" >How do I keep traces safe?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.agentixlabs.com\/blog\/general\/agent-observability-for-tool-using-agents-stop-costly-loops\/#How_much_should_I_sample\" >How much should I sample?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Why_this_suddenly_feels_urgent\"><\/span>Why this suddenly feels urgent<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You\u2019re on call at 9:47 PM. A \u201chelpful\u201d agent just updated 300 CRM records, and now Sales is yelling because half the fields look off. However, your service dashboards stay green because the API never went down.<\/p>\n<p>Meanwhile, the agent is quietly looping through tool calls, timing out on a flaky enrichment endpoint, and burning tokens like a space heater. That\u2019s the moment observability for agents stops being a nice-to-have and becomes survival gear.<\/p>\n<p>In short, it means you can explain what your agent did, step by step, and prove why it did it. Better yet, you can catch failures early, before they become costly incidents.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"In_this_article_youll_learn%E2%80%A6\"><\/span>In this article you\u2019ll learn\u2026<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li>Which signals actually explain agent behavior in production.<\/li>\n<li>A minimal instrumentation setup you can implement this week.<\/li>\n<li>How to trace tool calls, memory, and guardrails end to end.<\/li>\n<li>How to detect cost spikes and risky actions before they spread.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"What_agent_observability_includes_and_what_it_doesnt\"><\/span>What agent observability includes (and what it doesn\u2019t)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Traditional observability answers, \u2018Is the service up?\u2019 Agents add harder questions. \u2018Did it choose the right tool?\u2019 \u2018Did it act safely?\u2019<\/p>\n<p>MarkTechPost defines it this way. \u201cAgent observability is the discipline of instrumenting, tracing, evaluating, and monitoring AI agents across their full lifecycle.\u201d That lifecycle includes planning, tool use, and memory. It is not just prompts and responses.<\/p>\n<p>So, treat one agent run like a small distributed system. You want to see the internal steps, the external dependencies, and the quality checks that kept the output honest.<\/p>\n<ul>\n<li>Planning and routing decisions.<\/li>\n<li>Each tool call, including success rate and latency.<\/li>\n<li>Memory reads and writes, with safety controls.<\/li>\n<li>Guardrail events, approvals, and policy blocks.<\/li>\n<li>Evaluation signals tied to correctness and outcomes.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Trend_signals_why_teams_are_changing_their_monitoring_stack\"><\/span>Trend signals: why teams are changing their monitoring stack<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Even if you\u2019re not chasing hype, the ground is shifting under production agent teams. Several patterns keep showing up.<\/p>\n<ul>\n<li><strong>OpenTelemetry is becoming the backbone.<\/strong> Standard traces make multi-step runs debuggable across services and teams, and they reduce \u201ccustom glue\u201d monitoring.<\/li>\n<li><strong>Tool-using agents are scaling faster than expected.<\/strong> MCP-native SDKs and control planes make tool calling easier, which means more production blast radius when something breaks.<\/li>\n<li><strong>Governance expectations are rising.<\/strong> IBM notes that agents can act \u201cwithout constant human oversight,\u201d so audit trails and approvals are moving into the default design.<\/li>\n<li><strong>Evaluation is moving into production.<\/strong> Because agents are non-deterministic, uptime does not imply correctness, and silent failures are common.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"The_minimal_viable_signal_set_MVSS_you_should_capture\"><\/span>The minimal viable signal set (MVSS) you should capture<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you log everything, you\u2019ll drown. Instead, start with a small schema you can apply to every agent run, regardless of framework.<\/p>\n<p>First, make every run and step addressable. Then, capture what explains behavior: tool choice, tool results, latency, and cost.<\/p>\n<ul>\n<li><strong>run_id<\/strong>, <strong>step_id<\/strong>, and <strong>parent_step_id<\/strong> (for retries and branches).<\/li>\n<li><strong>agent_name<\/strong>, <strong>agent_version<\/strong>, and <strong>prompt_version<\/strong>.<\/li>\n<li><strong>model<\/strong>, <strong>temperature<\/strong>, and <strong>max_tokens<\/strong> (config drives behavior).<\/li>\n<li><strong>tool_name<\/strong> and <strong>tool_request_hash<\/strong> (hash inputs to avoid storing raw payloads).<\/li>\n<li><strong>tool_status<\/strong> and <strong>tool_latency_ms<\/strong>.<\/li>\n<li><strong>retry_count<\/strong> (so you can spot loops).<\/li>\n<li><strong>tokens_in<\/strong>, <strong>tokens_out<\/strong>, and <strong>cost_estimate<\/strong> per step.<\/li>\n<li><strong>guardrail_event<\/strong> and <strong>policy_outcome<\/strong> (allowed, blocked, needs approval).<\/li>\n<li><strong>user_outcome<\/strong> (resolved, escalated, wrote data, no-op).<\/li>\n<\/ul>\n<p>In addition, store raw prompts or tool payloads only if you can redact safely. Otherwise, keep hashes plus sampled payloads for failures.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"A_simple_tracing_model_map_one_run_to_one_trace\"><\/span>A simple tracing model: map one run to one trace<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Think of an agent run as a trace, and each step as a span. Tool calls become child spans. Memory operations and guardrails are spans too, because they change behavior.<\/p>\n<p>As a result, when something goes wrong, you can replay the story. You stop guessing, and you start diagnosing.<\/p>\n<ol>\n<li><strong>Ingress span.<\/strong> Request metadata, auth context, and rate-limit decisions.<\/li>\n<li><strong>Planning span.<\/strong> Goal interpretation, tool shortlist, and chosen strategy.<\/li>\n<li><strong>Tool spans.<\/strong> One span per tool call, including structured errors.<\/li>\n<li><strong>Memory spans.<\/strong> Retrieval queries, sources, and writebacks.<\/li>\n<li><strong>Guardrail spans.<\/strong> Redaction, policy checks, and approvals.<\/li>\n<li><strong>Egress span.<\/strong> Final response, actions taken, and outcome summary.<\/li>\n<\/ol>\n<p>Moreover, attach events for key decisions, like \u201cchose_tool=X because Y,\u201d or \u201cblocked_action due to policy Z.\u201d Those events are pure gold during incident review.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Two_real-world_failure_modes_and_the_signals_that_catch_them\"><\/span>Two real-world failure modes (and the signals that catch them)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Most agent incidents are not dramatic model hallucinations. Instead, they look like normal operations until you connect the dots across steps.<\/p>\n<p><strong>Mini case 1: \u201cPerfect uptime,\u201d shocking spend.<\/strong> A support agent handled tickets fine for days. Then a prompt tweak increased the average reasoning chain from 4 steps to 11. Consequently, token usage doubled, and nobody noticed until the invoice arrived.<\/p>\n<ul>\n<li>What catches it: tokens per run, tokens per step, and cost per successful outcome.<\/li>\n<li>What fixes it: a budget guardrail that stops loops and escalates early.<\/li>\n<\/ul>\n<p><strong>Mini case 2: The hallucinated tool argument trap.<\/strong> A proposal agent started sending malformed JSON into a pricing tool. The tool returned 400 errors, but the agent kept retrying with \u201cclose enough\u201d payloads. However, the logs looked like random noise because the payloads were not structured.<\/p>\n<ul>\n<li>What catches it: schema validation events and structured tool error codes.<\/li>\n<li>What fixes it: validate tool inputs before the call and fail fast on repeatable 4xx errors.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Common_mistakes_teams_make_even_strong_teams\"><\/span>Common mistakes teams make (even strong teams)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Teams rarely fail because they picked the \u201cwrong vendor.\u201d More often, they skip the boring parts that make debugging possible.<\/p>\n<ul>\n<li>Logging only the final answer, not the intermediate steps.<\/li>\n<li>Treating tool failures as \u201cexternal,\u201d so no alerts exist.<\/li>\n<li>Not versioning prompts, tools, and agent configs.<\/li>\n<li>Measuring uptime, but not correctness or user outcomes.<\/li>\n<li>Storing sensitive data without redaction or access controls.<\/li>\n<\/ul>\n<p>Also, watch out for the \u201cone big dashboard\u201d trap. If everything is on one chart, nothing is actionable.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Risks_how_observability_can_backfire\"><\/span>Risks: how observability can backfire<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Observability is supposed to reduce risk. Yet if you implement it carelessly, it can create new problems.<\/p>\n<ul>\n<li><strong>Privacy risk.<\/strong> Traces can capture PII from prompts, retrieval snippets, or tool payloads.<\/li>\n<li><strong>Security risk.<\/strong> Logs can leak API keys, tokens, or internal URLs if you are not careful.<\/li>\n<li><strong>Cost risk.<\/strong> High-cardinality logs and long retention can get expensive fast.<\/li>\n<li><strong>Noise risk.<\/strong> Too many metrics can hide the one signal that matters at 2 AM.<\/li>\n<\/ul>\n<p>Therefore, start with redaction, sampling, and role-based access from day one. It\u2019s not glamorous, but it beats an incident postmortem.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"A_quick_decision_guide_what_to_instrument_first\"><\/span>A quick decision guide: what to instrument first<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you need results fast, instrument in the order that reduces incidents the most.<\/p>\n<ol>\n<li><strong>Tool call telemetry.<\/strong> Track success rate, latency, retries, and error codes.<\/li>\n<li><strong>Token and cost tracking.<\/strong> Measure tokens per step and per outcome.<\/li>\n<li><strong>Run-level tracing.<\/strong> One trace per run with spans for steps, tools, memory, and guardrails.<\/li>\n<li><strong>High-risk action logging.<\/strong> Approvals, blocks, and who approved what.<\/li>\n<li><strong>Online evaluation.<\/strong> Sample, score, and review correctness over time.<\/li>\n<\/ol>\n<p>Next, expand based on your incident history. In contrast, don\u2019t add metrics \u201cjust in case.\u201d<\/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>If you want a low-drama rollout, start with one workflow and instrument it end to end. Then scale out to other agents using the same run and step schema.<\/p>\n<ul>\n<li>Pick one tool-heavy path and add tracing spans around every tool call.<\/li>\n<li>Add two alerts: tool failure rate and cost per successful outcome.<\/li>\n<li>Define one \u201cbad write\u201d or \u201cbad action\u201d metric for your domain.<\/li>\n<li>Run a weekly review of failed traces with engineering and the business owner.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.agentixlabs.com\/\">Explore more agent engineering guides on Agentix Labs<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Where_%E2%80%9Cobserveit_agent%E2%80%9D_fits_and_why_you_should_care\"><\/span>Where \u201cobserveit agent\u201d fits (and why you should care)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you\u2019ve seen searches for <strong>observeit agent<\/strong>, the intent is simple. \u201cI want to see what this agent did.\u201d Even if that product isn\u2019t in your stack, the need is real.<\/p>\n<p>So, treat it as a reminder to design for explainability. In practice, the best \u201cobserve it\u201d experience comes from run-level traces, structured tool logs, and a tight set of outcome metrics.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Further_reading\"><\/span>Further reading<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you want deeper context and industry framing, these sources are useful starting points.<\/p>\n<ul>\n<li><a href=\"https:\/\/www.ibm.com\/think\/insights\/ai-agent-observability\">IBM Think: AI agent observability overview<\/a>.<\/li>\n<li><a href=\"https:\/\/www.marktechpost.com\/2025\/08\/31\/what-is-ai-agent-observability-top-7-best-practices-for-reliable-ai\/\">MarkTechPost: Agent observability best practices<\/a>.<\/li>\n<li><a href=\"https:\/\/www.marktechpost.com\/2025\/10\/18\/kong-releases-volcano-a-typescript-mcp-native-sdk-for-building-production-ready-ai-agents-with-llm-reasoning-and-real-world-actions\/\">MarkTechPost: MCP-native SDK ecosystem signal<\/a>.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"Whats_the_difference_between_monitoring_and_observability_for_agents\"><\/span>What\u2019s the difference between monitoring and observability for agents?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Monitoring tells you something is wrong. Observability helps you explain why, using traces, logs, and agent-specific signals across steps and tools.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Do_I_need_OpenTelemetry_to_start\"><\/span>Do I need OpenTelemetry to start?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Not strictly. However, OpenTelemetry makes traces more portable and reduces custom instrumentation over time, especially across many services.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Whats_the_first_metric_worth_alerting_on\"><\/span>What\u2019s the first metric worth alerting on?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Tool call failure rate is often the fastest reliability signal. Next, alert on cost per successful outcome, not cost per request.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_do_I_measure_correctness_if_the_agent_is_non-deterministic\"><\/span>How do I measure correctness if the agent is non-deterministic?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Use a mix of automated evaluations, sampled human review, and user feedback. Then tie those scores to outcomes like resolution rate or bad-write rate.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_do_I_keep_traces_safe\"><\/span>How do I keep traces safe?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Redact PII, encrypt at rest, and limit access. Also, avoid storing raw tool payloads unless you have a clear need and a retention policy.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_much_should_I_sample\"><\/span>How much should I sample?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Start by keeping full traces for failures and a small sample for successes, like 5-10%. Then tune based on cost and incident frequency.<\/p>\n<span class=\"et_bloom_bottom_trigger\"><\/span>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>A practical checklist to trace tool calls, control token spend, and catch risky agent behavior before it hits production users.<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":2203,"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-2204","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\/2204","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=2204"}],"version-history":[{"count":0,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/posts\/2204\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/media\/2203"}],"wp:attachment":[{"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=2204"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=2204"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=2204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}