{"id":2209,"date":"2026-03-02T13:51:49","date_gmt":"2026-03-02T13:51:49","guid":{"rendered":"https:\/\/www.agentixlabs.com\/blog\/general\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/"},"modified":"2026-03-02T13:51:49","modified_gmt":"2026-03-02T13:51:49","slug":"how-to-evaluate-tool-calling-ai-agents-before-they-hit-production","status":"publish","type":"post","link":"https:\/\/www.agentixlabs.com\/blog\/general\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/","title":{"rendered":"How to Evaluate Tool-Calling AI Agents Before They Hit Production","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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#Why_%E2%80%9Cit_worked_in_the_demo%E2%80%9D_isnt_a_release_strategy\" >Why \u201cit worked in the demo\u201d isn\u2019t a release strategy<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#What_makes_tool-using_agents_harder_to_evaluate\" >What makes tool-using agents harder to evaluate<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#A_practical_scorecard_the_6_dimensions_that_matter_in_production\" >A practical scorecard: the 6 dimensions that matter in production<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#1_Task_success_did_the_job_get_done\" >1) Task success (did the job get done?)<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#2_Tool_correctness_selection_parameters_and_sequencing\" >2) Tool correctness (selection, parameters, and sequencing)<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#3_Groundedness_and_data_integrity_no_creative_writing_with_customer_data\" >3) Groundedness and data integrity (no creative writing with customer data)<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#4_Safety_and_policy_compliance_the_non-negotiables\" >4) Safety and policy compliance (the non-negotiables)<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#5_Latency_and_reliability_fast_enough_stable_enough\" >5) Latency and reliability (fast enough, stable enough)<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#6_Cost_per_successful_task_the_KPI_that_stops_fights\" >6) Cost per successful task (the KPI that stops fights)<\/a><\/li><\/ul><\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#A_simple_checklist_you_can_run_before_every_agent_release\" >A simple checklist you can run before every agent release<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#Two_mini_case_studies_what_%E2%80%9Cgood%E2%80%9D_looks_like\" >Two mini case studies (what \u201cgood\u201d looks like)<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#Common_mistakes_that_make_scorecards_fail\" >Common mistakes that make scorecards fail<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#Risks_what_can_go_wrong_if_you_evaluate_poorly\" >Risks: what can go wrong if you evaluate poorly<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#A_2-week_rollout_plan_for_continuous_agent_evaluation\" >A 2-week rollout plan for continuous agent evaluation<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#What_to_do_next_practical_steps_you_can_take_this_week\" >What to do next (practical steps you can take this week)<\/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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#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\/how-to-evaluate-tool-calling-ai-agents-before-they-hit-production\/#Further_reading\" >Further reading<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Why_%E2%80%9Cit_worked_in_the_demo%E2%80%9D_isnt_a_release_strategy\"><\/span>Why \u201cit worked in the demo\u201d isn\u2019t a release strategy<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You\u2019re in a Monday release meeting. The agent looked great on Friday, but today it calls the wrong tool, loops twice, and burns $18 in API spend to book one meeting. Everyone stares at the logs like they\u2019re tea leaves.<\/p>\n<p>If that scene feels familiar, you don\u2019t need more vibes. You need <strong>agent evaluation scorecards<\/strong> that turn reliability, safety, and cost into a repeatable go or no-go gate.<\/p>\n<div>\n<p><strong>In this article you\u2019ll learn:<\/strong><\/p>\n<ul>\n<li>What a production-ready agent scorecard should measure (and what to ignore).<\/li>\n<li>How to test tool selection, parameters, and recovery, not just \u201cgood answers\u201d.<\/li>\n<li>A simple 2-week rollout plan using offline tests plus real production traces.<\/li>\n<li>Common mistakes that make scorecards useless.<\/li>\n<li>What to do next to operationalize continuous evaluation.<\/li>\n<\/ul>\n<\/div>\n<p>Maxim AI sums up the direction of travel well: \u201cEvaluating AI agents in production requires comprehensive platforms that cover simulation, testing, and monitoring across the agent lifecycle.\u201d<\/p>\n<p><a href=\"https:\/\/www.getmaxim.ai\/articles\/top-4-ai-agent-evaluation-tools-in-2025\/\">Read the Maxim AI eval overview.<\/a><\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_makes_tool-using_agents_harder_to_evaluate\"><\/span>What makes tool-using agents harder to evaluate<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A chatbot can be judged on helpfulness and correctness. A tool-using agent is more like a junior operator with a keyboard and permissions. That changes what \u201cquality\u201d means.<\/p>\n<p>In practice, tool-using agents fail in a few boring but expensive ways. They pick the wrong tool, pass a malformed parameter, or recover badly after a tool error. Moreover, they can be \u201cconfidently wrong\u201d while still sounding polished.<\/p>\n<p>So your evaluation must cover two layers:<\/p>\n<ul>\n<li><strong>Outcome quality.<\/strong> Did the task complete to spec?<\/li>\n<li><strong>Process integrity.<\/strong> Did the agent choose the right tools, use them correctly, and handle failure safely?<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"A_practical_scorecard_the_6_dimensions_that_matter_in_production\"><\/span>A practical scorecard: the 6 dimensions that matter in production<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Here\u2019s a scorecard structure that works for most internal workflow agents and many customer-facing assistants. It\u2019s also compatible with a continuous evaluation loop, which is where the industry is heading.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"1_Task_success_did_the_job_get_done\"><\/span>1) Task success (did the job get done?)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>This is your north star. However, define it like an SRE would, not like a marketer would.<\/p>\n<ul>\n<li><strong>Definition.<\/strong> The agent completed the user\u2019s task within scope.<\/li>\n<li><strong>How to measure.<\/strong> Pass rate on an offline test set, plus spot checks on recent traces.<\/li>\n<li><strong>Threshold.<\/strong> Example: 85%+ pass rate on \u201cstandard\u201d cases, 95%+ on \u201csafe + simple\u201d cases.<\/li>\n<li><strong>Owner.<\/strong> Product or ops lead.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"2_Tool_correctness_selection_parameters_and_sequencing\"><\/span>2) Tool correctness (selection, parameters, and sequencing)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Tool correctness is where most production pain hides. The agent can write a perfect explanation and still call \u201cDeleteCustomer\u201d instead of \u201cUpdateCustomer\u201d. That is a bad day.<\/p>\n<ul>\n<li><strong>Tool selection accuracy.<\/strong> Did it choose the correct tool for the intent?<\/li>\n<li><strong>Parameter correctness.<\/strong> Did it pass valid, complete, and safe arguments?<\/li>\n<li><strong>Tool sequencing.<\/strong> Did it call tools in the right order, with the right checks?<\/li>\n<li><strong>Recovery quality.<\/strong> When a tool fails, did it retry safely, degrade gracefully, or ask a clarifying question?<\/li>\n<\/ul>\n<p>Score these separately. Otherwise you\u2019ll argue about a single \u201ctool score\u201d forever.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"3_Groundedness_and_data_integrity_no_creative_writing_with_customer_data\"><\/span>3) Groundedness and data integrity (no creative writing with customer data)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>If the agent reads from a CRM, ticketing system, or knowledge base, you need to know whether its output is anchored to real records. In addition, you need to ensure it does not leak or mutate data in unintended ways.<\/p>\n<ul>\n<li><strong>Groundedness.<\/strong> Does the final answer cite or reflect retrieved records correctly?<\/li>\n<li><strong>Staleness risk.<\/strong> Is the agent using cached data when it shouldn\u2019t?<\/li>\n<li><strong>Write safety.<\/strong> Are mutations gated, logged, and reversible?<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"4_Safety_and_policy_compliance_the_non-negotiables\"><\/span>4) Safety and policy compliance (the non-negotiables)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Some checks should be pass or fail. For example, actions that touch money, permissions, or regulated data deserve hard gates.<\/p>\n<ul>\n<li><strong>Allowed actions only.<\/strong> No tool calls outside the approved list.<\/li>\n<li><strong>PII handling.<\/strong> No unnecessary data exposure in responses or logs.<\/li>\n<li><strong>Escalation behavior.<\/strong> Clear handoff when confidence is low or risk is high.<\/li>\n<\/ul>\n<p>Keep this crisp. If your \u201csafety\u201d section is a novel, nobody will run it before a release.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"5_Latency_and_reliability_fast_enough_stable_enough\"><\/span>5) Latency and reliability (fast enough, stable enough)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Users don\u2019t care that your chain-of-thought is beautiful. They care that it finishes before their coffee gets cold.<\/p>\n<ul>\n<li><strong>Latency.<\/strong> p50 and p95 end-to-end task time.<\/li>\n<li><strong>Timeout rate.<\/strong> How often does it stall or exceed budgets?<\/li>\n<li><strong>Error rate.<\/strong> Tool errors, parsing failures, model failures.<\/li>\n<\/ul>\n<h3><span class=\"ez-toc-section\" id=\"6_Cost_per_successful_task_the_KPI_that_stops_fights\"><\/span>6) Cost per successful task (the KPI that stops fights)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Token cost alone is misleading. A cheap agent that fails twice and escalates is still expensive. Consequently, normalize cost by success.<\/p>\n<ul>\n<li><strong>Metric.<\/strong> Dollars per successful task, not dollars per conversation.<\/li>\n<li><strong>Budget.<\/strong> A per-task ceiling tied to business value.<\/li>\n<li><strong>Guardrail.<\/strong> Maximum retries and tool calls per task.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"A_simple_checklist_you_can_run_before_every_agent_release\"><\/span>A simple checklist you can run before every agent release<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you want something you can paste into a release ticket, start here. It\u2019s intentionally \u201cboring\u201d, because boring is shippable.<\/p>\n<ul>\n<li>Offline test set run completed and archived, with pass\/fail examples attached.<\/li>\n<li>Tool selection accuracy measured on at least 50 representative cases.<\/li>\n<li>Parameter validation failures are under the agreed threshold.<\/li>\n<li>High-risk intents trigger escalation or require confirmation.<\/li>\n<li>p95 latency and timeout rate meet the release budget.<\/li>\n<li>Cost per successful task is below the ceiling on a fixed workload sample.<\/li>\n<li>Rollback plan documented (model, prompt, tool version, and flags).<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Two_mini_case_studies_what_%E2%80%9Cgood%E2%80%9D_looks_like\"><\/span>Two mini case studies (what \u201cgood\u201d looks like)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>These examples are simplified, but the mechanics are real.<\/p>\n<p><strong>Case 1: Sales ops \u201cCRM updater\u201d agent.<\/strong> The agent takes inbound form fills and updates HubSpot fields. Offline accuracy looked fine, but production traces showed a new failure mode: it mapped \u201cCompany size: 11-50\u201d into the wrong enum value when the CRM schema changed. The fix was not a better prompt. It was a scorecard check for <em>parameter correctness against the current schema<\/em>, plus a nightly regression run.<\/p>\n<p><strong>Case 2: Support \u201crefund eligibility\u201d agent.<\/strong> The agent read policy docs and decided whether to approve refunds. It was accurate, but it sometimes skipped the required \u201corder verification\u201d tool call. The team added a process-integrity metric: for refund intents, the verification tool must be called before any decision. Pass rate improved, and so did audit confidence.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Common_mistakes_that_make_scorecards_fail\"><\/span>Common mistakes that make scorecards fail<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A scorecard is only useful if it changes decisions. Here are the mistakes that quietly kill them.<\/p>\n<ul>\n<li><strong>Scoring only the final answer.<\/strong> Tool-using agents need process metrics, or you\u2019ll miss the real failures.<\/li>\n<li><strong>No fixed test set.<\/strong> If you can\u2019t replay the same cases, you can\u2019t spot regressions.<\/li>\n<li><strong>Testing \u201chappy paths\u201d only.<\/strong> Real users are messy, vague, and sometimes adversarial.<\/li>\n<li><strong>Using one blended quality number.<\/strong> You need separate levers for safety, success, latency, and cost.<\/li>\n<li><strong>Not assigning owners.<\/strong> If nobody owns a metric, it becomes trivia.<\/li>\n<li><strong>Ignoring drift.<\/strong> A scorecard snapshot ages like milk once inputs and tools change.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Risks_what_can_go_wrong_if_you_evaluate_poorly\"><\/span>Risks: what can go wrong if you evaluate poorly<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Skipping evaluation is an obvious risk. However, <em>bad evaluation<\/em> can be worse because it creates false confidence.<\/p>\n<ul>\n<li><strong>Hidden unsafe actions.<\/strong> The agent behaves in policy during tests, but takes risky actions with different phrasing in production.<\/li>\n<li><strong>Cost explosions.<\/strong> A retry loop or tool failure cascade can multiply spend fast.<\/li>\n<li><strong>Silent data corruption.<\/strong> Wrong fields updated, wrong records merged, wrong permissions applied.<\/li>\n<li><strong>Compliance exposure.<\/strong> PII leaks into logs, or policy-required steps are skipped.<\/li>\n<li><strong>Incentive traps.<\/strong> Optimizing tokens reduces reasoning, which can reduce success and increase escalations.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"A_2-week_rollout_plan_for_continuous_agent_evaluation\"><\/span>A 2-week rollout plan for continuous agent evaluation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You don\u2019t need a six-month program. You need momentum and a loop that survives staffing changes. Here\u2019s a realistic plan.<\/p>\n<ol>\n<li><strong>Days 1-2: Define \u201csuccess\u201d and \u201cunsafe\u201d.<\/strong> First, write 10 examples of success and 10 examples of \u201cmust escalate\u201d. Get alignment.<\/li>\n<li><strong>Days 3-5: Build a fixed test set.<\/strong> Next, collect 50-150 cases from tickets, chats, or workflow logs. Include edge cases.<\/li>\n<li><strong>Days 6-7: Add tool correctness checks.<\/strong> Then, log tool calls and validate parameters. Flag forbidden tools and sequences.<\/li>\n<li><strong>Days 8-10: Add cost and latency budgets.<\/strong> After that, compute cost per successful task and p95 latency on the test set.<\/li>\n<li><strong>Days 11-12: Start a rolling trace sample.<\/strong> In addition, pick 20-50 recent production traces weekly for review and regression.<\/li>\n<li><strong>Days 13-14: Turn it into a release gate.<\/strong> Finally, add thresholds and owners, and require a scorecard snapshot in every release.<\/li>\n<\/ol>\n<p>For tool and platform context, Maxim AI notes that evaluation is increasingly lifecycle-based, spanning simulation, testing, and monitoring.<\/p>\n<p><a href=\"https:\/\/www.agentixlabs.com\/\">Agent evaluation services<\/a><\/p>\n<h2><span class=\"ez-toc-section\" id=\"What_to_do_next_practical_steps_you_can_take_this_week\"><\/span>What to do next (practical steps you can take this week)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If you\u2019re shipping an agent in the next 30 days, do these steps in order. They\u2019re small, but they compound.<\/p>\n<ul>\n<li><strong>Pick one workflow.<\/strong> Choose the agent that touches real systems, not the demo bot.<\/li>\n<li><strong>Create a 1-page scorecard.<\/strong> Define each dimension, how you measure it, and the threshold.<\/li>\n<li><strong>Freeze a baseline test set.<\/strong> Store inputs and expected outcomes. Treat it like code.<\/li>\n<li><strong>Instrument tool calls.<\/strong> Log tool name, parameters, results, and retries.<\/li>\n<li><strong>Add a weekly eval ritual.<\/strong> Review failures, update the test set, and decide what ships.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>1) How big should my offline test set be?<\/strong><br \/>Start with 50-150 cases. However, prioritize diversity over volume. Add cases whenever you see new failure modes.<\/p>\n<p><strong>2) Can I automate the entire scorecard?<\/strong><br \/>Not safely. Automated metrics are great for pass rates, tool schemas, latency, and cost. In contrast, edge cases and safety often need human review.<\/p>\n<p><strong>3) What\u2019s the difference between evaluation and observability?<\/strong><br \/>Observability tells you what happened in production. Evaluation tells you whether what happened is acceptable, and whether it regressed. They should share the same traces.<\/p>\n<p><strong>4) How do I score tool selection accuracy?<\/strong><br \/>Label a sample of intents with the correct tool. Then compare the agent\u2019s tool choice to the label. Review disagreements, because labels can be wrong too.<\/p>\n<p><strong>5) What thresholds should I use for go\/no-go?<\/strong><br \/>Use hard gates for safety and compliance. For quality and cost, use budgets and trend lines. As a result, you can ship improvements without ignoring risk.<\/p>\n<p><strong>6) How often should I rerun evals?<\/strong><br \/>At minimum, rerun on any model, prompt, tool, or policy change. In addition, run weekly on a rolling sample of production traces to catch drift.<\/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><a href=\"https:\/\/www.getmaxim.ai\/articles\/top-4-ai-agent-evaluation-tools-in-2025\/\">Top 4 AI Agent Evaluation Tools in 2025 (Maxim AI).<\/a><\/li>\n<li><a href=\"https:\/\/o-mega.ai\/articles\/the-best-ai-agent-evals-and-benchmarks-full-2025-guide\">Best AI Agent Evaluation Benchmarks: 2025 Guide (o-mega).<\/a><\/li>\n<li>Authoritative docs to consult: your model provider\u2019s tool-calling documentation, your security team\u2019s logging and PII handling standards, and internal incident postmortems.<\/li>\n<\/ul>\n<span class=\"et_bloom_bottom_trigger\"><\/span>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>Use a practical agent scorecard to measure success, tool correctness, safety, and cost per task, with a simple 2-week rollout plan.<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":2208,"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-2209","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\/2209","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=2209"}],"version-history":[{"count":0,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/posts\/2209\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/media\/2208"}],"wp:attachment":[{"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=2209"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=2209"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.agentixlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=2209"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}