![\"\"](\"\/wp-content\/uploads\/2025\/03\/image-12.png\")
<\/figure>\n\n\n\nModel: Merkezi Karar Verici<\/h3>\n\n\n\n
Agent kapsam\u0131ndaki “model” terimi, genellikle merkezi karar verici olarak g\u00f6rev yapan bir dil modelini ifade eder. Bu model, agent\u2019in i\u00e7sel mant\u0131ksal s\u00fcre\u00e7lerini y\u00f6nlendirir ve aksiyonlar\u0131n\u0131 \u015fekillendirir. Kullan\u0131lan modeller farkl\u0131 boyutlarda olabilir (k\u00fc\u00e7\u00fck ya da b\u00fcy\u00fck) ve instruction-based reasoning yeteneklerine sahip olmal\u0131d\u0131r. Bu ba\u011flamda ReAct<\/strong>, Chain-of-Thought (CoT)<\/strong> ya da Tree-of-Thoughts<\/strong> gibi ak\u0131l y\u00fcr\u00fctme yakla\u015f\u0131mlar\u0131 \u00f6ne \u00e7\u0131kar.<\/p>\n\n\n\n Model, genel ama\u00e7l\u0131, multimodal ya da belirli bir g\u00f6rev i\u00e7in fine-tuned olabilir. Ancak dikkat edilmesi gereken \u00f6nemli bir nokta \u015fudur: Model, agent\u2019in spesifik tool se\u00e7imleri ya da orchestration konfig\u00fcrasyonlar\u0131yla do\u011frudan e\u011fitilmemi\u015ftir. Bu nedenle, modelin agent\u2019e \u00f6zel g\u00f6revlerde daha etkili hale gelmesi i\u00e7in, \u00f6rneklerle (\u00f6rne\u011fin belirli bir tool\u2019un nas\u0131l kullan\u0131ld\u0131\u011f\u0131na dair senaryolarla) desteklenmesi \u00f6nerilir.<\/p>\n\n\n\n <\/p>\n\n\n\n Bir dil modeli kendi ba\u015f\u0131na etkileyici sonu\u00e7lar \u00fcretebilirken, ger\u00e7ek d\u00fcnyadaki sistemlerle etkile\u015fime ge\u00e7mesi s\u0131n\u0131rl\u0131d\u0131r. \u0130\u015fte bu noktada devreye tools<\/strong> girer. Tool\u2019lar, agent\u2019in harici veri kaynaklar\u0131na veya servislerine eri\u015fmesini sa\u011flar ve modelin tek ba\u015f\u0131na ger\u00e7ekle\u015ftiremeyece\u011fi i\u015flemleri m\u00fcmk\u00fcn k\u0131lar.<\/p>\n\n\n\n Tool\u2019lar sayesinde agent, veri \u00e7ekebilir, API \u00e7a\u011fr\u0131s\u0131 yapabilir, harici sistemlere aksiyon g\u00f6nderebilir ya da \u00f6zel g\u00f6revleri otomatik olarak yerine getirebilir. Bu da yaln\u0131zca i\u00e7eride d\u00fc\u015f\u00fcnen bir modelden ziyade, aksiyon alan bir yapay zek\u00e2 agent\u2019ine d\u00f6n\u00fc\u015f\u00fcm sa\u011flar.<\/p>\n\n\n\n <\/p>\n\n\n\n Orchestration layer, agent\u2019in bilgi alma, i\u00e7sel ak\u0131l y\u00fcr\u00fctme yapma ve buna dayal\u0131 olarak bir sonraki aksiyonu belirleme s\u00fcrecini d\u00f6ng\u00fcsel olarak y\u00f6neten katmand\u0131r. Bu d\u00f6ng\u00fc, agent bir hedefe ula\u015fana veya durma noktas\u0131na gelene kadar devam eder.<\/p>\n\n\n\n Bu yap\u0131 basit kurallara dayal\u0131 karar d\u00f6ng\u00fclerinden, zincirleme mant\u0131k ak\u0131\u015flar\u0131na ve hatta makine \u00f6\u011frenimi algoritmalar\u0131yla desteklenen daha kompleks reasoning yap\u0131lar\u0131na kadar de\u011fi\u015febilir. Orchestration layer\u2019in karma\u015f\u0131kl\u0131\u011f\u0131, agent\u2019in amac\u0131 ve g\u00f6revine g\u00f6re \u015fekillenir.<\/p>\n\n\n\n <\/p>\n\n\n\n A\u015fa\u011f\u0131daki tablo, geleneksel dil modelleri ile modern AI agent yap\u0131lar\u0131 aras\u0131ndaki temel farklar\u0131 \u00f6zetlemektedir:<\/p>\n\n\n\n <\/p>\n\n\n\n Genel dil modelleri tek ba\u015flar\u0131na g\u00fc\u00e7l\u00fc ara\u00e7lar olabilir; ancak ger\u00e7ek d\u00fcnyadaki g\u00f6revleri otonom \u015fekilde yerine getirebilmek i\u00e7in agent mimarilerine ihtiya\u00e7 vard\u0131r. Model, tool ve orchestration layer bile\u015fenlerinin entegre \u00e7al\u0131\u015fmas\u0131 sayesinde bu sistemler, yaln\u0131zca bilgi \u00fcretmekle kalmaz, ayn\u0131 zamanda ak\u0131l y\u00fcr\u00fctebilir, karar verebilir ve aksiyon alabilir h\u00e2le gelir. Bu mimari, yapay zek\u00e2n\u0131n bir \u00fcst seviyeye ta\u015f\u0131nmas\u0131nda kritik bir rol oynamaktad\u0131r.<\/p>\n","protected":false},"excerpt":{"rendered":" Yapay zek\u00e2 destekli agent mimarileri, sadece g\u00fc\u00e7l\u00fc dil modelleri (Language Models – LM) de\u011fil, ayn\u0131 zamanda bu modellerin d\u0131\u015f d\u00fcnya ile etkile\u015fime ge\u00e7mesini sa\u011flayan tool\u2019lar ve ak\u0131ll\u0131 karar alma s\u00fcre\u00e7lerini y\u00f6neten bir orchestration layer sayesinde etkili hale gelir. Bu yaz\u0131da, agent mimarisinin \u00fc\u00e7 temel yap\u0131 ta\u015f\u0131 olan model, tool ve orchestration layer kavramlar\u0131na odaklanaca\u011f\u0131z. Model:\u2026 Read More »<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":348,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[149,153,150,133],"_links":{"self":[{"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=\/wp\/v2\/posts\/350"}],"collection":[{"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=350"}],"version-history":[{"count":1,"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=\/wp\/v2\/posts\/350\/revisions"}],"predecessor-version":[{"id":352,"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=\/wp\/v2\/posts\/350\/revisions\/352"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=\/wp\/v2\/media\/348"}],"wp:attachment":[{"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=350"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=350"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.firatyasar.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=350"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Tool: Agent\u2019i Ger\u00e7ek D\u00fcnya ile Bulu\u015fturan K\u00f6pr\u00fc<\/h3>\n\n\n\n
Orchestration Layer: D\u00fc\u015f\u00fcnme D\u00f6ng\u00fcs\u00fcn\u00fc Y\u00f6neten Katman<\/h3>\n\n\n\n
Model ve Agent\u2019ler Aras\u0131ndaki Temel Farklar<\/h3>\n\n\n\n
\u00d6zellik<\/th> Modeller<\/th> Agent\u2019ler<\/th><\/tr><\/thead> Bilgi Kayna\u011f\u0131<\/strong><\/td> Bilgi, yaln\u0131zca e\u011fitim verisi ile s\u0131n\u0131rl\u0131d\u0131r.<\/td> Bilgi, harici sistemlerle tool\u2019lar arac\u0131l\u0131\u011f\u0131yla geni\u015fletilir.<\/td><\/tr> Sorguya Yan\u0131t<\/strong><\/td> Kullan\u0131c\u0131 sorgusuna g\u00f6re tek seferlik tahmin yap\u0131l\u0131r. Modelde \u00f6zel olarak uygulanmad\u0131ysa oturum ge\u00e7mi\u015fi ya da s\u00fcrekli ba\u011flam y\u00f6netimi yoktur (\u00f6r. sohbet ge\u00e7mi\u015fi).<\/td> Oturum ge\u00e7mi\u015fi y\u00f6netilir (\u00f6r. sohbet ge\u00e7mi\u015fi) ve \u00e7ok ad\u0131ml\u0131 tahminler yap\u0131labilir. Bu yap\u0131, orchestration layer i\u00e7inde yap\u0131lan sorgular ve kararlar \u00fczerinden y\u00fcr\u00fct\u00fcl\u00fcr. Bu ba\u011flamda, bir \u201cturn\u201d, sistemle agent aras\u0131ndaki bir etkile\u015fim olarak tan\u0131mlan\u0131r (\u00f6r. 1 sorgu ve 1 yan\u0131t).<\/td><\/tr> Tool Kullan\u0131m\u0131<\/strong><\/td> Yerel tool entegrasyonu bulunmaz.<\/td> Tool\u2019lar, agent mimarisine do\u011fal olarak entegre edilmi\u015ftir.<\/td><\/tr> Mant\u0131ksal Yap\u0131<\/strong><\/td> Yerel bir mant\u0131k katman\u0131 bulunmaz. Kullan\u0131c\u0131lar, basit sorular veya CoT, ReAct gibi reasoning framework\u2019leriyle olu\u015fturulmu\u015f karma\u015f\u0131k prompt\u2019larla modeli y\u00f6nlendirebilir.<\/td> CoT, ReAct veya LangChain gibi haz\u0131r agent framework\u2019lerini kullanan yerle\u015fik bili\u015fsel mimariye sahiptir.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Sonu\u00e7<\/h2>\n\n\n\n