{"id":2932,"date":"2026-06-09T17:36:52","date_gmt":"2026-06-09T14:36:52","guid":{"rendered":"https:\/\/shareai.now\/?p=2932"},"modified":"2026-06-09T17:36:55","modified_gmt":"2026-06-09T14:36:55","slug":"ai-araci-kosum-takimi-uretim-calisma-zamani","status":"publish","type":"post","link":"https:\/\/shareai.now\/tr\/blog\/gelistiriciler\/ai-araci-kosum-takimi-uretim-calisma-zamani\/","title":{"rendered":"AI Arac\u0131 Ko\u015fum Tak\u0131m\u0131: \u00dcretim Ajanlar\u0131n\u0131n \u0130htiya\u00e7 Duydu\u011fu \u00c7al\u0131\u015fma Zaman\u0131 Katman\u0131"},"content":{"rendered":"<p>Bir <strong>AI ajan ko\u015fum tak\u0131m\u0131<\/strong> bir modeli, ara\u00e7lar\u0131, talimatlar\u0131 ve kullan\u0131c\u0131 hedeflerini \u00fcretim i\u015f ak\u0131\u015f\u0131na d\u00f6n\u00fc\u015ft\u00fcren \u00e7al\u0131\u015fma zaman\u0131 katman\u0131d\u0131r. Modelin kendisi de\u011fildir. Sadece bir ajan \u00e7er\u00e7evesi de\u011fildir. Ajan\u0131n etraf\u0131ndaki i\u015fletim katman\u0131d\u0131r: d\u00f6ng\u00fc, ara\u00e7 \u00e7a\u011fr\u0131lar\u0131, onaylar, kimlik bilgileri, ba\u011flam kontrolleri, sandboxing, izler ve ajan\u0131 \u00e7al\u0131\u015ft\u0131rmay\u0131 daha g\u00fcvenli hale getiren kullan\u0131m g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc.<\/p>\n\n\n\n<p>Bu ayr\u0131m, ekipler demolar\u0131n \u00f6tesine ge\u00e7ti\u011finde \u00f6nem kazan\u0131r. Bir prototip bir modeli ve bir arac\u0131 \u00e7a\u011f\u0131rabilir. Bir \u00fcretim ajan\u0131, depolara, dahili belgelere, m\u00fc\u015fteri kay\u0131tlar\u0131na, faturaland\u0131rma i\u015flemlerine, destek taleplerine veya i\u015f ak\u0131\u015f\u0131 sistemlerine dokunabilir. O noktada zor soru art\u0131k \u201changi modeli kullanmal\u0131y\u0131z?\u201d de\u011fil, \u201cmodeli hareket ederken hangi \u00e7al\u0131\u015fma zaman\u0131 kontrol ediyor?\u201d olur.\u201d<\/p>\n\n\n\n<p>ShareAI, model eri\u015fimi, y\u00f6nlendirme, hata tolerans\u0131 ve pazar g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc i\u00e7in AI pazar\u0131 ve API katman\u0131 olarak bu y\u0131\u011f\u0131na uyum sa\u011flar. Ekipler <a href=\"https:\/\/shareai.now\/models\/?utm_source=blog&amp;utm_medium=content&amp;utm_campaign=ai-agent-harness-production-runtime\">modelleri kar\u015f\u0131la\u015ft\u0131rabilir<\/a>, trafi\u011fi tek bir API \u00fczerinden y\u00f6nlendirebilir ve model kullan\u0131m\u0131n\u0131 \u00f6l\u00e7\u00fclebilir tutarken \u00e7evresindeki uygulama veya ko\u015fum tak\u0131m\u0131 ShareAI d\u0131\u015f\u0131nda kal\u0131r.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Bir AI ajan ko\u015fum tak\u0131m\u0131 asl\u0131nda ne yapar<\/h2>\n\n\n\n<p>Bir AI ajan ko\u015fum tak\u0131m\u0131, bir model etraf\u0131ndaki y\u00fcr\u00fctme d\u00f6ng\u00fcs\u00fcn\u00fc y\u00f6netir. Yayg\u0131n desen planlama, hareket etme, g\u00f6zlemleme ve devam edip etmeme karar\u0131n\u0131 i\u00e7erir. Ko\u015fum tak\u0131m\u0131 model \u00e7a\u011fr\u0131lar\u0131n\u0131 g\u00f6nderir, ara\u00e7lar\u0131 \u00e7al\u0131\u015ft\u0131r\u0131r, ara\u00e7 sonu\u00e7lar\u0131n\u0131 al\u0131r, ba\u011flam\u0131 g\u00fcnceller ve g\u00f6rev tamamland\u0131\u011f\u0131nda veya bir s\u0131n\u0131r a\u015f\u0131ld\u0131\u011f\u0131nda durur.<\/p>\n\n\n\n<p>\u00c7al\u0131\u015fma zaman\u0131 ayr\u0131ca \u00fcretim ajanlar\u0131n\u0131 sohbet botlar\u0131ndan farkl\u0131 k\u0131lan b\u00f6l\u00fcmleri de y\u00f6netir: ara\u00e7 izinleri, gizli bilgi y\u00f6netimi, riskli eylemler i\u00e7in onaylar, g\u00f6zlemlenebilirlik, maliyet takibi, durum, yeniden denemeler ve sandboxed y\u00fcr\u00fctme. Bu katman olmadan, her ekip genellikle her ajan etraf\u0131nda ayn\u0131 k\u0131r\u0131lgan tesisat\u0131 yeniden in\u015fa eder.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Model eri\u015fimi:<\/strong> g\u00f6rev i\u00e7in do\u011fru modeli se\u00e7mek ve \u00e7a\u011f\u0131rmak.<\/li>\n<li><strong>Ara\u00e7 y\u00f6nlendirme:<\/strong> ajan\u0131 API'lere, MCP ara\u00e7lar\u0131na, veri tabanlar\u0131na, dosyalara veya kod y\u00fcr\u00fctmeye ba\u011flamak.<\/li>\n<li><strong>Ba\u011flam kontrol\u00fc:<\/strong> uzun s\u00fcreli \u00e7al\u0131\u015fmay\u0131 faydal\u0131 bir model ba\u011flam penceresi i\u00e7inde tutmak.<\/li>\n<li><strong>Onaylar:<\/strong> y\u0131k\u0131c\u0131 veya hassas i\u015flemleri \u00e7al\u0131\u015ft\u0131r\u0131lmadan \u00f6nce durdurma.<\/li>\n<li><strong>Kimlik bilgisi y\u00f6netimi:<\/strong> sa\u011flay\u0131c\u0131 anahtarlar\u0131n\u0131 ve ara\u00e7 jetonlar\u0131n\u0131 ajan istemlerinden ve yap\u0131land\u0131rmalar\u0131ndan uzak tutma.<\/li>\n<li><strong>G\u00f6zlemlenebilirlik:<\/strong> model \u00e7a\u011fr\u0131lar\u0131n\u0131, ara\u00e7 \u00e7a\u011fr\u0131lar\u0131n\u0131, gecikmeyi, jetonlar\u0131 ve \u00e7al\u0131\u015fma ba\u015f\u0131na maliyeti izleme.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Neden harness ger\u00e7ek bir yap\u0131m veya sat\u0131n alma karar\u0131d\u0131r<\/h2>\n\n\n\n<p>Model \u00e7a\u011fr\u0131lar\u0131 nispeten basittir. Ara\u00e7 tan\u0131mlar\u0131 giderek standartla\u015fmaktad\u0131r. Pahal\u0131 olan k\u0131s\u0131m model etraf\u0131ndaki tekrarlanabilir \u00e7al\u0131\u015fma zaman\u0131d\u0131r: sandbox ya\u015fam d\u00f6ng\u00fcs\u00fc, yeniden denemeler, b\u00fct\u00e7eler, onaylar, denetim g\u00fcnl\u00fckleri, izinler, ba\u011flam s\u0131k\u0131\u015ft\u0131rma ve ad\u0131m ba\u015f\u0131na maliyet g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc.<\/p>\n\n\n\n<p>Her dahili ekip bu harness'i ba\u011f\u0131ms\u0131z olarak olu\u015fturursa, her ekip ayn\u0131 zamanda farkl\u0131 bir g\u00fcvenlik modeline sahip olur. Biri g\u00fc\u00e7l\u00fc denetim g\u00fcnl\u00fcklerine sahip olabilir ancak kimlik bilgisi hijyeninde zay\u0131f olabilir. Bir di\u011feri ara\u00e7 eri\u015fimine sahip olabilir ancak onay kap\u0131lar\u0131 olmayabilir. \u00dc\u00e7\u00fcnc\u00fcs\u00fc bir i\u015f ak\u0131\u015f\u0131 i\u00e7in iyi \u00e7al\u0131\u015fabilir ancak uzun bir g\u00f6rev ba\u011flam penceresini doldurdu\u011funda ba\u015far\u0131s\u0131z olabilir.<\/p>\n\n\n\n<p>Payla\u015f\u0131lan bir harness, platform ekiplerine \u00e7al\u0131\u015fma zaman\u0131 beklentilerini tan\u0131mlamak i\u00e7in tek bir yer sa\u011flar. Uygulama ekipleri hala ajan talimatlar\u0131n\u0131, i\u015f ak\u0131\u015flar\u0131n\u0131 ve \u00fcr\u00fcn mant\u0131\u011f\u0131n\u0131 sahiplenir, ancak ortak kontroller s\u0131f\u0131rdan yeniden olu\u015fturulmak zorunda de\u011fildir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">De\u011ferlendirilecek AI ajan harness yetenekleri<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Yetenek<\/th><th>Neden \u00f6nemli<\/th><\/tr><\/thead><tbody><tr><td>Merkezi model y\u00f6nlendirme<\/td><td>Ekiplerin bir sa\u011flay\u0131c\u0131y\u0131 sabitlemek yerine fiyat, gecikme, kullan\u0131labilirlik ve g\u00f6rev uyumuna g\u00f6re modeller se\u00e7mesine olanak tan\u0131r.<\/td><\/tr><tr><td>Ara\u00e7 y\u00f6netimi<\/td><td>Ajan\u0131n hangi ara\u00e7lar\u0131, hangi kimlik alt\u0131nda ve hangi izinlerle \u00e7a\u011f\u0131rabilece\u011fini kontrol eder.<\/td><\/tr><tr><td>Onay kap\u0131lar\u0131<\/td><td>\u0130nsan onay\u0131 olmadan geri \u00f6demeler, silmeler, da\u011f\u0131t\u0131mlar veya veri de\u011fi\u015fiklikleri gibi hassas i\u015flemleri durdurur.<\/td><\/tr><tr><td>Kimlik bilgisi izolasyonu<\/td><td>API anahtarlar\u0131n\u0131 ve belirte\u00e7lerini istemlerden, ajan tan\u0131mlar\u0131ndan, g\u00fcnl\u00fcklerden ve depolardan uzak tutar.<\/td><\/tr><tr><td>Kum havuzu<\/td><td>Ajan\u0131n ana ortam\u0131na do\u011frudan eri\u015fim sa\u011flamadan kod veya dosya i\u015flemlerine izin verir.<\/td><\/tr><tr><td>U\u00e7tan uca izleme<\/td><td>Her \u00e7al\u0131\u015fmada ne oldu\u011funu, model \u00e7a\u011fr\u0131lar\u0131n\u0131, ara\u00e7 \u00e7a\u011fr\u0131lar\u0131n\u0131, belirte\u00e7leri, gecikmeyi ve maliyeti g\u00f6sterir.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Modelin <a href=\"https:\/\/modelcontextprotocol.io\/specification\/2024-11-05\/index?utm_source=shareai.now&amp;utm_medium=content&amp;utm_campaign=ai-agent-harness-production-runtime\">Model Ba\u011flam Protokol\u00fc<\/a> bu katman\u0131n daha \u00f6nemli hale gelmesinin bir nedenidir. MCP, AI uygulamalar\u0131na ara\u00e7lar, kaynaklar ve istemlerle daha tutarl\u0131 bir \u015fekilde ba\u011flanma imkan\u0131 verir. Bu tutarl\u0131l\u0131k faydal\u0131d\u0131r, ancak ayn\u0131 zamanda ara\u00e7 eri\u015fiminin bir y\u00f6netim modeli gerektirdi\u011fi anlam\u0131na gelir. Harness, bu ara\u00e7lar\u0131n nas\u0131l se\u00e7ildi\u011fine, yetkilendirildi\u011fine, g\u00f6zlemlendi\u011fine ve s\u0131n\u0131rland\u0131r\u0131ld\u0131\u011f\u0131na karar verir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">ShareAI'nin bir ajan harness y\u0131\u011f\u0131n\u0131na nas\u0131l uydu\u011fu<\/h2>\n\n\n\n<p>ShareAI bir ajan harness de\u011fildir ve sizin i\u00e7in uygulama veya ajan olu\u015fturmaz. Model eri\u015fimi ve kullan\u0131m g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc gerektiren bir ajan, \u00fcr\u00fcn, eklenti, i\u015f ak\u0131\u015f\u0131 veya kendi bar\u0131nd\u0131r\u0131lan uygulaman\u0131n arkas\u0131nda yer alabilecek AI pazar\u0131 ve API katman\u0131d\u0131r.<\/p>\n\n\n\n<p>Ajanlar olu\u015fturan ekipler i\u00e7in ShareAI'yi \u00fc\u00e7 pratik \u015fekilde kullan\u0131\u015fl\u0131 hale getirir.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Model eri\u015fimi i\u00e7in tek bir API:<\/strong> her sa\u011flay\u0131c\u0131y\u0131 ayr\u0131 ayr\u0131 ba\u011flamak yerine tek bir entegrasyonla 150+ modele ba\u011flan\u0131n.<\/li>\n<li><strong>Y\u00f6nlendirme ve hata tolerans\u0131:<\/strong> uygulama bu kontrolleri kullanacak \u015fekilde tasarland\u0131\u011f\u0131nda, model se\u00e7imi, fiyat, gecikme, kullan\u0131labilirlik ve g\u00fcvenilirlik sinyallerine g\u00f6re istekleri y\u00f6nlendirin.<\/li>\n<li><strong>Kullan\u0131m g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc:<\/strong> ekiplerin maliyet, trafik desenleri ve \u00fcr\u00fcn davran\u0131\u015f\u0131 hakk\u0131nda d\u00fc\u015f\u00fcnebilmesi i\u00e7in model t\u00fcketimini \u00f6l\u00e7\u00fclebilir tutun.<\/li>\n<\/ul>\n\n\n\n<p>Yap\u0131c\u0131lar, ShareAI'nin d\u0131\u015f\u0131nda sahip olduklar\u0131 bir uygulaman\u0131n par\u00e7as\u0131 olan bir ajan oldu\u011funda da ShareAI'yi kullanabilir. Bu durumda, Yap\u0131c\u0131 AI \u00e7\u0131kar\u0131m trafi\u011fini ShareAI \u00fczerinden y\u00f6nlendirir, bir ek \u00fccret veya marj belirler, m\u00fc\u015fterilerin y\u00f6nlendirilen kullan\u0131m i\u00e7in ShareAI'ye \u00f6deme yapmas\u0131na izin verir ve olu\u015fturulan kazan\u00e7lara g\u00f6re ayl\u0131k \u00f6demeler al\u0131r. Uygulama ShareAI'nin d\u0131\u015f\u0131nda olu\u015fturulmu\u015f ve kontrol edilmi\u015ftir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u00dcretim ajan\u0131 \u00e7al\u0131\u015ft\u0131rmalar\u0131nda neyi izlemeli<\/h2>\n\n\n\n<p>\u00dcretim ajanlar\u0131n\u0131n yaln\u0131zca istek g\u00fcnl\u00fcklerinden fazlas\u0131na ihtiyac\u0131 vard\u0131r. Faydal\u0131 bir izleme, bir \u00e7al\u0131\u015fman\u0131n s\u0131ral\u0131 ad\u0131mlar\u0131n\u0131 g\u00f6stermelidir: model \u00e7a\u011fr\u0131lar\u0131, ara\u00e7 \u00e7a\u011fr\u0131lar\u0131, onaylar, sandbox eylemleri, yeniden denemeler, token say\u0131lar\u0131, gecikme ve maliyet. OpenTelemetry izlemeleri, ebeveyn-\u00e7ocuk ili\u015fkileriyle ba\u011flanm\u0131\u015f span koleksiyonlar\u0131 olarak tan\u0131mlar, bu da ajan \u00e7al\u0131\u015fmalar\u0131 i\u00e7in de faydal\u0131 bir zihinsel modeldir: her ajan ad\u0131m\u0131 daha b\u00fcy\u00fck g\u00f6rev i\u00e7inde izlenebilir olmal\u0131d\u0131r.<\/p>\n\n\n\n<p>Ajan ekipleri i\u00e7in hedef basittir. Bir \u015feyler ters gitti\u011finde \u015fu sorular\u0131 yan\u0131tlayabilmelisiniz: hangi model yan\u0131t verdi, hangi ara\u00e7 \u00e7a\u011fr\u0131ld\u0131, hangi veri iletildi, kim onaylad\u0131, ka\u00e7 token kullan\u0131ld\u0131, ne kadar s\u00fcrd\u00fc ve maliyeti neydi. <a href=\"https:\/\/opentelemetry.io\/docs\/reference\/specification\/overview\/?utm_source=shareai.now&amp;utm_medium=content&amp;utm_campaign=ai-agent-harness-production-runtime\">OpenTelemetry spesifikasyonu<\/a> hizmetler aras\u0131nda g\u00f6zlemlenebilirli\u011fi standartla\u015ft\u0131ran ekipler i\u00e7in faydal\u0131 bir referans noktas\u0131d\u0131r.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Yayg\u0131n AI ajan\u0131 y\u00f6netim hatalar\u0131<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Ajan tan\u0131mlar\u0131na s\u0131rlar koymak:<\/strong> s\u0131rlar, istemler, yap\u0131land\u0131rmalar ve yeniden kullan\u0131labilir ajan \u015fablonlar\u0131n\u0131n d\u0131\u015f\u0131nda y\u00f6netilmelidir.<\/li>\n<li><strong>T\u00fcm ara\u00e7lar\u0131 g\u00fcvenli olarak kabul etmek:<\/strong> salt okunur ara\u00e7lar, yazma ara\u00e7lar\u0131 ve y\u0131k\u0131c\u0131 ara\u00e7lar farkl\u0131 kontroller gerektirir.<\/li>\n<li><strong>Kullan\u0131c\u0131 ba\u015f\u0131na izlemeyi atlamak:<\/strong> Payla\u015f\u0131lan anahtarlar, bir model \u00e7a\u011fr\u0131s\u0131n\u0131 veya ara\u00e7 eylemini kimin ger\u00e7ekle\u015ftirdi\u011fini denetlemeyi zorla\u015ft\u0131r\u0131r.<\/li>\n<li><strong>Fatura gelene kadar maliyeti g\u00f6rmezden gelmek:<\/strong> Yeniden denemeler, ara\u00e7 sonu\u00e7lar\u0131 ve uzun ba\u011flam y\u00f6netilmedi\u011finde, ajan d\u00f6ng\u00fcleri jeton kullan\u0131m\u0131n\u0131 h\u0131zla art\u0131rabilir.<\/li>\n<li><strong>Her ekibin kendi \u00e7al\u0131\u015fma zaman\u0131n\u0131 olu\u015fturmas\u0131na izin vermek:<\/strong> Yinelenen harness \u00e7al\u0131\u015fmas\u0131, tutars\u0131z y\u00f6netim ve dengesiz g\u00fcvenilirlik yarat\u0131r.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">ShareAI ile ne zaman ba\u015flanmal\u0131<\/h2>\n\n\n\n<p>Ajan veya uygulama, harness karar\u0131 tamamen netle\u015fmeden \u00f6nce esnek model eri\u015fimine ihtiya\u00e7 duydu\u011funda ShareAI ile ba\u015flayabilirsiniz. <a href=\"https:\/\/console.shareai.now\/chat\/?utm_source=shareai.now&amp;utm_medium=content&amp;utm_campaign=ai-agent-harness-production-runtime\">Playground'da<\/a> Model davran\u0131\u015f\u0131n\u0131 test etmek, pazardaki model se\u00e7eneklerini g\u00f6zden ge\u00e7irmek ve <a href=\"https:\/\/shareai.now\/documentation\/?utm_source=blog&amp;utm_medium=content&amp;utm_campaign=ai-agent-harness-production-runtime\">Belgeler<\/a> bir API'yi entegre etmeye haz\u0131r oldu\u011funuzda kullanabilirsiniz.<\/p>\n\n\n\n<p>\u00dcr\u00fcn ekipleri i\u00e7in temiz mimari genellikle katmanl\u0131d\u0131r. Uygulama, kullan\u0131c\u0131 deneyimine sahiptir. Harness, ajan \u00e7al\u0131\u015fma zaman\u0131 davran\u0131\u015f\u0131n\u0131 y\u00f6netir. ShareAI, AI model eri\u015fimini, y\u00f6nlendirmeyi, pazar sinyallerini, faturaland\u0131rmay\u0131 ve bu yeteneklerin i\u015f ak\u0131\u015f\u0131na uydu\u011fu yerde kullan\u0131m g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fcn\u00fc ele al\u0131r.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">SSS<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">AI ajan harness nedir?<\/h3>\n\n\n<p>AI ajan harness, bir modelin etraf\u0131ndaki \u00e7al\u0131\u015fma zaman\u0131 katman\u0131d\u0131r. Ajan d\u00f6ng\u00fcs\u00fcn\u00fc, ara\u00e7 \u00e7a\u011fr\u0131lar\u0131n\u0131, ba\u011flam\u0131, kimlik bilgilerini, onaylar\u0131, sandboxing'i, izlemeyi ve maliyet g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fcn\u00fc y\u00f6netir.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">AI ajan harness, bir ajan \u00e7er\u00e7evesi ile ayn\u0131 m\u0131?<\/h3>\n\n\n<p>Hay\u0131r. Bir \u00e7er\u00e7eve, geli\u015ftiricilerin ajan davran\u0131\u015f\u0131n\u0131 tan\u0131mlamas\u0131na yard\u0131mc\u0131 olur. Bir harness, \u00fcretimde bu davran\u0131\u015f\u0131 izinler, izler, onaylar ve \u00e7al\u0131\u015fma zaman\u0131 s\u0131n\u0131rlar\u0131 gibi kontrollerle \u00e7al\u0131\u015ft\u0131r\u0131r ve y\u00f6netir.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">ShareAI, bir AI ajan harness i\u00e7inde nerede yer al\u0131r?<\/h3>\n\n\n<p>ShareAI, model eri\u015fimi, y\u00f6nlendirme, hata tolerans\u0131, kullan\u0131m g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc ve faturaland\u0131rma i\u00e7in yapay zeka pazar\u0131 ve API katman\u0131 olarak uyum sa\u011flar. Arac\u0131 veya uygulama ShareAI d\u0131\u015f\u0131nda olu\u015fturulur.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">ShareAI bir arac\u0131 harness'\u0131n\u0131 de\u011fi\u015ftirebilir mi?<\/h3>\n\n\n<p>Hay\u0131r. ShareAI tam arac\u0131 \u00e7al\u0131\u015fma zaman\u0131n\u0131 sa\u011flamaz. Bir arac\u0131 harness'\u0131 veya uygulaman\u0131n \u00e7a\u011f\u0131rd\u0131\u011f\u0131 model eri\u015fim ve y\u00f6nlendirme katman\u0131n\u0131 destekleyebilir.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">\u00dcretim ara\u00e7lar\u0131n\u0131n neden onay kap\u0131lar\u0131na ihtiyac\u0131 var?<\/h3>\n\n\n<p>Onay kap\u0131lar\u0131, bir arac\u0131n\u0131n veri silme, geri \u00f6deme yapma, kod da\u011f\u0131tma, kay\u0131tlar\u0131 de\u011fi\u015ftirme veya ayr\u0131cal\u0131kl\u0131 ara\u00e7lar\u0131 \u00e7a\u011f\u0131rma gibi hassas i\u015flemleri ger\u00e7ekle\u015ftirebilece\u011fi durumlarda riski azalt\u0131r.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Kimlik bilgileri neden arac\u0131 tan\u0131mlar\u0131ndan uzak tutulmal\u0131?<\/h3>\n\n\n<p>Arac\u0131 tan\u0131mlar\u0131ndaki kimlik bilgileri, depolar, g\u00fcnl\u00fckler, d\u0131\u015fa aktar\u0131mlar veya kopyalanm\u0131\u015f yap\u0131land\u0131rmalar arac\u0131l\u0131\u011f\u0131yla s\u0131zabilir. \u00dcretim sistemleri kimlik bilgilerini dolayl\u0131 olarak referans almal\u0131 ve onaylanm\u0131\u015f \u00e7al\u0131\u015fma zaman\u0131 kontrolleri arac\u0131l\u0131\u011f\u0131yla enjekte etmelidir.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">MCP arac\u0131 harness tasar\u0131m\u0131n\u0131 nas\u0131l de\u011fi\u015ftirir?<\/h3>\n\n\n<p>MCP, ara\u00e7 ve ba\u011flam ba\u011flant\u0131lar\u0131n\u0131 daha standart hale getirir. Bu, hangi ara\u00e7lar\u0131n izinli oldu\u011fu, nas\u0131l kimlik do\u011frulamas\u0131 yap\u0131ld\u0131\u011f\u0131 ve \u00e7a\u011fr\u0131lar\u0131n nas\u0131l denetlendi\u011fini y\u00f6neten bir harness veya ge\u00e7it katman\u0131na olan ihtiyac\u0131 art\u0131r\u0131r.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Tak\u0131mlar arac\u0131 \u00e7al\u0131\u015ft\u0131rmalar\u0131nda neyi izlemeli?<\/h3>\n\n\n<p>Tak\u0131mlar model \u00e7a\u011fr\u0131lar\u0131n\u0131, ara\u00e7 \u00e7a\u011fr\u0131lar\u0131n\u0131, onaylar\u0131, hatalar\u0131, token kullan\u0131m\u0131n\u0131, gecikmeyi, maliyeti, kullan\u0131c\u0131 atamas\u0131n\u0131 ve nihai \u00e7\u0131kt\u0131y\u0131 izlemelidir. Bu sinyaller olmadan hatalar\u0131 \u00e7\u00f6zmek zordur.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Model y\u00f6nlendirme yapay zeka ara\u00e7lar\u0131 i\u00e7in faydal\u0131 m\u0131?<\/h3>\n\n\n<p>Evet. Farkl\u0131 arac\u0131 ad\u0131mlar\u0131 farkl\u0131 modellere ihtiya\u00e7 duyabilir. Y\u00f6nlendirme, her ad\u0131m\u0131 varsay\u0131lan bir modele g\u00f6ndermek yerine maliyet, gecikme, eri\u015filebilirlik ve kaliteyi dengelemeye yard\u0131mc\u0131 olabilir.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Yap\u0131c\u0131lar ShareAI ile arac\u0131 kullan\u0131m\u0131n\u0131 paraya \u00e7evirebilir mi?<\/h3>\n\n\n<p>Evet, Yap\u0131c\u0131 ShareAI d\u0131\u015f\u0131nda bir uygulamaya sahip oldu\u011funda ve yapay zeka \u00e7\u0131kar\u0131m trafi\u011fini ShareAI \u00fczerinden y\u00f6nlendirdi\u011finde. Yap\u0131c\u0131 bir marj veya ek \u00fccret belirleyebilir ve olu\u015fturulan kullan\u0131m temelinde ayl\u0131k \u00f6demeler alabilir.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Model eri\u015fimini test etmek i\u00e7in ilk ad\u0131m nedir?<\/h3>\n\n\n<p>Modelleri test etmek i\u00e7in ShareAI Playground'u kullan\u0131n, ard\u0131ndan uygulaman\u0131zdan veya ajan \u00e7al\u0131\u015fma zaman\u0131ndan model \u00e7a\u011fr\u0131lar\u0131n\u0131 ba\u011flamaya haz\u0131r oldu\u011funuzda bir API anahtar\u0131 olu\u015fturun.<\/p>","protected":false},"excerpt":{"rendered":"<p>AI ajan\u0131 kontrol katman\u0131 i\u00e7in pratik bir rehber: \u00e7al\u0131\u015fma zaman\u0131 kontrol\u00fc, ara\u00e7 y\u00f6netimi, y\u00f6nlendirme, g\u00f6zlemlenebilirlik ve ShareAI'nin nas\u0131l uydu\u011fu.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cta-title":"Integrate one API","cta-description":"Access 150+ models with smart routing and failover.","cta-button-text":"View Docs","cta-button-link":"https:\/\/shareai.now\/documentation\/?utm_source=blog&amp;utm_medium=content&amp;utm_campaign=ai-agent-harness-production-runtime","rank_math_title":"AI Agent Harness: The Runtime Layer Production Agents Need","rank_math_description":"AI agent harness guide for production teams: runtime duties, tool governance, routing, observability, and where ShareAI fits.","rank_math_focus_keyword":"AI agent harness","footnotes":""},"categories":[4,6],"tags":[89,99],"class_list":["post-2932","post","type-post","status-publish","format-standard","hentry","category-developers","category-insights","tag-agentic-workflows","tag-ai-agents"],"_links":{"self":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/2932","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/comments?post=2932"}],"version-history":[{"count":1,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/2932\/revisions"}],"predecessor-version":[{"id":2933,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/2932\/revisions\/2933"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/media?parent=2932"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/categories?post=2932"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/tags?post=2932"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}