Yapay zeka teknolojilerinin h\u0131zla geli\u015fti\u011fi g\u00fcn\u00fcm\u00fczde, AI ajanlar\u0131 otomasyon, karar verme ve karma\u015f\u0131k g\u00f6revlerin \u00fcstesinden gelme yetenekleriyle dikkat \u00e7ekiyor. Kendi yapay zeka ajan\u0131 projenize ba\u015flamak, hem ki\u015fisel geli\u015fiminiz i\u00e7in harika bir f\u0131rsat sunar hem de bu heyecan verici alana derinlemesine dalman\u0131z\u0131 sa\u011flar. Bu makale, ilk AI ajan\u0131 projenizi ba\u015far\u0131yla hayata ge\u00e7irmeniz i\u00e7in gerekli temel bilgileri, ad\u0131mlar\u0131 ve ipu\u00e7lar\u0131n\u0131 sunarak size kapsaml\u0131 bir rehberlik edecektir.<\/p>\n
Yapay zeka ajanlar\u0131, \u00e7evrelerini alg\u0131layabilen, bu alg\u0131lara dayanarak ak\u0131l y\u00fcr\u00fctebilen ve belirli hedeflere ula\u015fmak i\u00e7in eylemlerde bulunabilen otonom sistemlerdir. Basit bir kural tabanl\u0131 robottan, karma\u015f\u0131k dil modelleriyle desteklenen ak\u0131ll\u0131 asistanlara kadar geni\u015f bir yelpazeyi kapsarlar. Kendi ba\u015flar\u0131na karar verme ve \u00f6\u011frenme yetenekleri, onlar\u0131 geleneksel yaz\u0131l\u0131mlardan ay\u0131r\u0131r.<\/p>\n
Bir yapay zeka ajan\u0131, temel olarak d\u00f6rt ana bile\u015fene sahiptir:<\/p>\n
Bu bile\u015fenler, ajan\u0131n s\u00fcrekli bir “alg\u0131la-d\u00fc\u015f\u00fcn-hareket et” d\u00f6ng\u00fcs\u00fcnde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar.<\/p>\n
Yapay zeka ajanlar\u0131, \u00e7evreleriyle etkile\u015fim bi\u00e7imlerine g\u00f6re farkl\u0131l\u0131k g\u00f6sterebilir:<\/p>\n
\u0130lk ajan\u0131n\u0131z\u0131 olu\u015ftururken genellikle reaktif veya basit proaktif yakla\u015f\u0131mlarla ba\u015flamak daha kolayd\u0131r.<\/p>\n
Bir yapay zeka ajan\u0131, genellikle a\u015fa\u011f\u0131daki ad\u0131mlar\u0131 izleyerek \u00e7al\u0131\u015f\u0131r:<\/p>\n
Kendi yapay zeka ajan\u0131 projenize giri\u015fmek, sadece teknik bir beceri kazanman\u0131n \u00f6tesinde bir\u00e7ok fayda sa\u011flar. Bu s\u00fcre\u00e7, hem ki\u015fisel hem de profesyonel geli\u015fiminiz i\u00e7in de\u011ferli bir deneyim sunar.<\/p>\n
Bir AI ajan\u0131 geli\u015ftirmek, yapay zeka, makine \u00f6\u011frenimi, programlama ve problem \u00e7\u00f6zme gibi alanlarda pratik bilgi edinmenin en iyi yollar\u0131ndan biridir. Teorik bilgileri ger\u00e7ek d\u00fcnya senaryolar\u0131na uygulama f\u0131rsat\u0131 bulur, kar\u015f\u0131la\u015ft\u0131\u011f\u0131n\u0131z zorluklarla ba\u015fa \u00e7\u0131karak analitik d\u00fc\u015f\u00fcnme becerilerinizi geli\u015ftirirsiniz. Bu s\u00fcre\u00e7, karma\u015f\u0131k sistemlerin nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 anlaman\u0131za yard\u0131mc\u0131 olur.<\/p>\n
AI ajanlar\u0131, tekrarlayan ve zaman al\u0131c\u0131 g\u00f6revleri otomatikle\u015ftirmek i\u00e7in m\u00fckemmel ara\u00e7lard\u0131r. Kendi ajan\u0131nz\u0131 olu\u015fturarak, g\u00fcnl\u00fck i\u015f ak\u0131\u015f\u0131n\u0131zdaki belirli g\u00f6revleri otomatik hale getirebilir, b\u00f6ylece daha stratejik ve yarat\u0131c\u0131 i\u015flere odaklanmak i\u00e7in zaman kazanabilirsiniz. \u00d6rne\u011fin, e-postalar\u0131 s\u0131n\u0131fland\u0131ran, toplant\u0131 \u00f6zetleri \u00e7\u0131karan veya belirli verileri toplayan bir ajan geli\u015ftirebilirsiniz.<\/p>\n
Kendi ajan\u0131nz\u0131 geli\u015ftirmek, hayal g\u00fcc\u00fcn\u00fcz\u00fc kullanarak yenilik\u00e7i \u00e7\u00f6z\u00fcmler \u00fcretmenize olanak tan\u0131r. Hobilerinizden i\u015f s\u00fcre\u00e7lerinize kadar geni\u015f bir alanda ki\u015fiselle\u015ftirilmi\u015f AI \u00e7\u00f6z\u00fcmleri tasarlayabilirsiniz. Oyun i\u00e7i karakterler, ak\u0131ll\u0131 ev asistanlar\u0131, ki\u015fisel finans dan\u0131\u015fmanlar\u0131 veya i\u00e7erik olu\u015fturma yard\u0131mc\u0131lar\u0131 gibi s\u0131n\u0131rs\u0131z uygulama alan\u0131 mevcuttur. Bu, teknolojiyle etkile\u015fim kurma \u015feklinizi d\u00f6n\u00fc\u015ft\u00fcrebilir.<\/p>\n
\u0130lk AI ajan\u0131 projenize ba\u015flamadan \u00f6nce baz\u0131 temel bilgilere ve ara\u00e7lara sahip olman\u0131z, s\u00fcreci \u00e7ok daha verimli hale getirecektir. Endi\u015felenmeyin, ba\u015flang\u0131\u00e7 seviyesinde olsan\u0131z bile bu alana ad\u0131m atabilirsiniz.<\/p>\n
Yapay zeka ve makine \u00f6\u011frenimi d\u00fcnyas\u0131nda Python, tart\u0131\u015fmas\u0131z en pop\u00fcler programlama dilidir. Geni\u015f k\u00fct\u00fcphane deste\u011fi, okunabilir s\u00f6zdizimi ve b\u00fcy\u00fck bir geli\u015ftirici toplulu\u011fu sayesinde AI ajan\u0131 geli\u015ftirmek i\u00e7in ideal bir se\u00e7imdir. Temel Python bilgisine (de\u011fi\u015fkenler, d\u00f6ng\u00fcler, ko\u015fullu ifadeler, fonksiyonlar, s\u0131n\u0131flar) sahip olmak, bu yolculukta size b\u00fcy\u00fck avantaj sa\u011flayacakt\u0131r.<\/p>\n
Modern AI ajanlar\u0131 genellikle B\u00fcy\u00fck Dil Modelleri (LLM’ler) etraf\u0131nda in\u015fa edilir. Bu modellerle etkile\u015fim kurmak ve ajan mant\u0131\u011f\u0131n\u0131 olu\u015fturmak i\u00e7in belirli k\u00fct\u00fcphanelere ihtiyac\u0131n\u0131z olacak:<\/p>\n
Verimli bir geli\u015ftirme s\u00fcreci i\u00e7in uygun bir ortam kurman\u0131z \u00f6nemlidir:<\/p>\n
venv<\/code> veya conda<\/code> gibi ara\u00e7larla sanal ortamlar kullan\u0131n.<\/li>\n- IDE\/Metin Edit\u00f6r\u00fc:<\/strong> Visual Studio Code, PyCharm veya Jupyter Notebook gibi bir geli\u015ftirme ortam\u0131 se\u00e7in. \u00d6zellikle Jupyter Notebook, iteratif geli\u015ftirme ve deneme yan\u0131lma i\u00e7in \u00e7ok kullan\u0131\u015fl\u0131d\u0131r.<\/li>\n
- API Anahtarlar\u0131:<\/strong> Kullanaca\u011f\u0131n\u0131z LLM API’leri i\u00e7in gerekli anahtarlar\u0131 g\u00fcvenli bir \u015fekilde saklay\u0131n ve ortam de\u011fi\u015fkenleri arac\u0131l\u0131\u011f\u0131yla eri\u015fin.<\/li>\n<\/ul>\n
Ajan Tasar\u0131m\u0131 ve Planlama<\/h3>\n
Ba\u015far\u0131l\u0131 bir AI ajan\u0131 projesi, iyi bir planlama ve tasar\u0131mla ba\u015flar. Acele etmeden, ajan\u0131n ne yapaca\u011f\u0131n\u0131, nas\u0131l \u00e7al\u0131\u015faca\u011f\u0131n\u0131 ve hangi ara\u00e7lar\u0131 kullanaca\u011f\u0131n\u0131 netle\u015ftirmek \u00f6nemlidir.<\/p>\n
Problemi Tan\u0131mlama ve Hedef Belirleme<\/h4>\n
\u0130lk ad\u0131m, ajan\u0131n \u00e7\u00f6zece\u011fi problemi veya ger\u00e7ekle\u015ftirece\u011fi g\u00f6revi net bir \u015fekilde tan\u0131mlamakt\u0131r. Ajan\u0131n\u0131z ne yapacak? Hangi ihtiyac\u0131 kar\u015f\u0131layacak? Hedefleriniz spesifik, \u00f6l\u00e7\u00fclebilir, ula\u015f\u0131labilir, ilgili ve zamana ba\u011fl\u0131 (SMART) olmal\u0131d\u0131r. \u00d6rne\u011fin, “Web’den belirli bir konu hakk\u0131nda bilgi toplay\u0131p \u00f6zetleyen bir ajan” veya “E-posta gelen kutusundaki spam’leri otomatik olarak filtreleyen bir ajan”.<\/p>\n
Ba\u015flang\u0131\u00e7 i\u00e7in basit ve tek bir amaca hizmet eden bir ajan se\u00e7mek, \u00f6\u011frenme s\u00fcrecinizi kolayla\u015ft\u0131racakt\u0131r.<\/p>\n
Ajan\u0131n Mimarisini \u00c7izme<\/h4>\n
Ajan\u0131n\u0131z\u0131n temel yap\u0131s\u0131n\u0131 ve bile\u015fenler aras\u0131 etkile\u015fimi g\u00f6rselle\u015ftirmek, karma\u015f\u0131kl\u0131\u011f\u0131 azalt\u0131r. A\u015fa\u011f\u0131daki sorular\u0131 yan\u0131tlayarak bir taslak olu\u015fturabilirsiniz:<\/p>\n
\n- Ajan hangi girdileri alacak? (Kullan\u0131c\u0131 metni, dosya, API yan\u0131t\u0131 vb.)<\/li>\n
- Hangi kararlar\u0131 verecek? (Hangi arac\u0131 kullanmal\u0131, hangi bilgiyi \u00f6zetlemeli vb.)<\/li>\n
- Hangi eylemleri ger\u00e7ekle\u015ftirecek? (API \u00e7a\u011fr\u0131s\u0131, dosya yazma, kullan\u0131c\u0131ya yan\u0131t verme vb.)<\/li>\n
- Belle\u011fe ihtiyac\u0131 var m\u0131? (Ge\u00e7mi\u015f konu\u015fmalar\u0131 hat\u0131rlamal\u0131 m\u0131?)<\/li>\n
- Hangi LLM’i kullanacak?<\/li>\n<\/ul>\n
Basit bir ak\u0131\u015f \u015femas\u0131 \u00e7izmek, bu a\u015famada \u00e7ok faydal\u0131 olacakt\u0131r.<\/p>\n
Kullan\u0131lacak Ara\u00e7lar ve Veri Kaynaklar\u0131<\/h4>\n
Ajan\u0131n\u0131z\u0131n hedeflerine ula\u015fmak i\u00e7in hangi d\u0131\u015f ara\u00e7lara veya veri kaynaklar\u0131na ihtiyac\u0131 olaca\u011f\u0131n\u0131 belirleyin. Bu ara\u00e7lar, ajan\u0131n “elleri” ve “g\u00f6zleri” gibidir:<\/p>\n
\n- Web Arama Arac\u0131:<\/strong> Google Search API, DuckDuckGo Search API.<\/li>\n
- Hesap Makinesi:<\/strong> Python’\u0131n yerle\u015fik matematik fonksiyonlar\u0131 veya \u00f6zel bir hesaplama arac\u0131.<\/li>\n
- Dosya Okuma\/Yazma:<\/strong> Yerel dosya sistemine eri\u015fim.<\/li>\n
- API \u00c7a\u011fr\u0131lar\u0131:<\/strong> Hava durumu API’si, d\u00f6viz kuru API’si vb.<\/li>\n
- Veritaban\u0131:<\/strong> Bilgiyi kal\u0131c\u0131 olarak saklamak i\u00e7in.<\/li>\n<\/ul>\n
Bu ara\u00e7lar\u0131 \u00f6nceden belirlemek, geli\u015ftirme a\u015famas\u0131nda size yol g\u00f6sterecektir.<\/p>\n
Ajan\u0131n Geli\u015ftirilmesi: Ad\u0131m Ad\u0131m Uygulama<\/h3>\n
\u015eimdi s\u0131ra, planlar\u0131m\u0131z\u0131 koda d\u00f6kmeye geldi. Bu b\u00f6l\u00fcmde, basit bir AI ajan\u0131 olu\u015fturmak i\u00e7in temel ad\u0131mlar\u0131 ve kod yap\u0131lar\u0131n\u0131 inceleyece\u011fiz. LangChain gibi framework’ler bu s\u00fcreci olduk\u00e7a kolayla\u015ft\u0131rsa da, temel mant\u0131\u011f\u0131 anlamak i\u00e7in sade bir \u00f6rnek \u00fczerinden ilerleyelim.<\/p>\n
Temel Yap\u0131y\u0131 Olu\u015fturma (LLM Entegrasyonu)<\/h4>\n
Ajan\u0131m\u0131z\u0131n beyni olacak LLM ile entegrasyon, ilk ad\u0131md\u0131r. Bir LLM’e girdi g\u00f6nderip \u00e7\u0131kt\u0131 alabilmeliyiz. A\u015fa\u011f\u0131daki \u00f6rnekte, OpenAI API’sini kullanarak basit bir LLM etkile\u015fimi g\u00f6sterilmi\u015ftir:<\/p>\n
\nimport os\nfrom openai import OpenAI\n\n# API anahtar\u0131n\u0131z\u0131 ortam de\u011fi\u015fkenlerinden al\u0131n\n# os.environ[\"OPENAI_API_KEY\"] = \"sk-...\" # Ger\u00e7ek anahtar\u0131n\u0131z\u0131 buraya yazmay\u0131n!\n\nclient = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\"))\n\ndef llm_query(prompt):\n try:\n response = client.chat.completions.create(\n model=\"gpt-3.5-turbo\", # Veya \"gpt-4\"\n messages=[\n {\"role\": \"system\", \"content\": \"Sen yard\u0131msever bir yapay zeka asistan\u0131s\u0131n.\"},\n {\"role\": \"user\", \"content\": prompt}\n ],\n temperature=0.7,\n max_tokens=150\n )\n return response.choices[0].message.content.strip()\n except Exception as e:\n return f\"LLM sorgusu s\u0131ras\u0131nda bir hata olu\u015ftu: {e}\"\n\n# Test edelim\n# print(llm_query(\"\u0130stanbul'un ba\u015fkenti neresidir?\"))\n<\/pre>\n<\/code><\/p>\n
Bu kod par\u00e7as\u0131, bir LLM'e nas\u0131l sorgu g\u00f6nderilece\u011fini ve yan\u0131t\u0131n nas\u0131l al\u0131naca\u011f\u0131n\u0131 g\u00f6sterir. Ajan\u0131m\u0131z\u0131n t\u00fcm kararlar\u0131 bu llm_query<\/code> fonksiyonu \u00fczerinden \u015fekillenecektir.<\/p>\nBellek (Memory) Ekleme<\/h4>\n
\u00c7o\u011fu ajan, ge\u00e7mi\u015f etkile\u015fimleri hat\u0131rlayarak daha tutarl\u0131 ve ba\u011flam odakl\u0131 yan\u0131tlar verebilir. Basit bir bellek mekanizmas\u0131, ge\u00e7mi\u015f konu\u015fmalar\u0131 bir liste olarak saklamak ve her yeni sorguda LLM'e bu ge\u00e7mi\u015fi de sunmakt\u0131r.<\/p>\n
\nclass SimpleMemory:\n def __init__(self):\n self.history = []\n\n def add_interaction(self, user_message, ai_response):\n self.history.append({\"role\": \"user\", \"content\": user_message})\n self.history.append({\"role\": \"assistant\", \"content\": ai_response})\n\n def get_history(self, limit=5): # Son 'limit' kadar etkile\u015fimi d\u00f6nd\u00fcr\n return self.history[-limit*2:] # Hem kullan\u0131c\u0131 hem de asistan mesajlar\u0131 i\u00e7in *2\n\n# Belle\u011fi kullanarak LLM sorgusunu g\u00fcncelleyelim\ndef llm_query_with_memory(prompt, memory):\n messages = [{\"role\": \"system\", \"content\": \"Sen yard\u0131msever bir yapay zeka asistan\u0131s\u0131n.\"}]\n \n # Ge\u00e7mi\u015fi ekle\n messages.extend(memory.get_history())\n \n # Mevcut kullan\u0131c\u0131 mesaj\u0131n\u0131 ekle\n messages.append({\"role\": \"user\", \"content\": prompt})\n\n try:\n response = client.chat.completions.create(\n model=\"gpt-3.5-turbo\",\n messages=messages,\n temperature=0.7,\n max_tokens=200\n )\n ai_response = response.choices[0].message.content.strip()\n memory.add_interaction(prompt, ai_response) # Belle\u011fe ekle\n return ai_response\n except Exception as e:\n return f\"LLM sorgusu s\u0131ras\u0131nda bir hata olu\u015ftu: {e}\"\n\n# Test edelim\n# my_memory = SimpleMemory()\n# print(llm_query_with_memory(\"Merhaba, nas\u0131ls\u0131n?\", my_memory))\n# print(llm_query_with_memory(\"Peki az \u00f6nce ne sordum?\", my_memory))\n<\/pre>\n<\/code><\/p>\n
Ara\u00e7lar (Tools) Tan\u0131mlama<\/h4>\n
Ajan\u0131m\u0131z\u0131n d\u0131\u015f d\u00fcnyayla etkile\u015fim kurmas\u0131n\u0131 sa\u011flayacak ara\u00e7lar\u0131 tan\u0131mlayal\u0131m. Bu ara\u00e7lar, ajan\u0131n belirli g\u00f6revleri yerine getirmesine olanak tan\u0131r. \u00d6rne\u011fin, bir web arama arac\u0131:<\/p>\n
\nimport requests\n\ndef search_web(query):\n # Bu, ger\u00e7ek bir arama motoru API'si ile de\u011fi\u015ftirilmelidir.\n # \u00d6rnek olarak, DuckDuckGo'nun basit bir arama URL'ini kullanal\u0131m.\n # Ger\u00e7ek uygulamalarda bir API anahtar\u0131 ve daha geli\u015fmi\u015f bir k\u00fct\u00fcphane kullan\u0131n.\n try:\n url = f\"https:\/\/api.duckduckgo.com\/?q={query}&format=json&pretty=1\"\n response = requests.get(url)\n data = response.json()\n if data and \"Abstract\" in data and data[\"Abstract\"]:\n return data[\"Abstract\"]\n elif data and \"RelatedTopics\" in data and data[\"RelatedTopics\"]:\n # \u0130lk ilgili konuyu alal\u0131m\n if isinstance(data[\"RelatedTopics\"], list) and len(data[\"RelatedTopics\"]) > 0:\n return data[\"RelatedTopics\"][0].get(\"Text\", \"Bilgi bulunamad\u0131.\")\n return \"Web'de bu konuda bilgi bulunamad\u0131.\"\n except Exception as e:\n return f\"Web aramas\u0131 s\u0131ras\u0131nda hata olu\u015ftu: {e}\"\n\n# Ara\u00e7lar\u0131 bir s\u00f6zl\u00fckte tutal\u0131m\ntools = {\n \"web_search\": search_web,\n # \"calculator\": lambda x: eval(x) # G\u00fcvenlik riski ta\u015f\u0131d\u0131\u011f\u0131 i\u00e7in dikkatli kullan\u0131lmal\u0131!\n}\n<\/pre>\n<\/code><\/p>\n
Ajan\u0131m\u0131z\u0131n hangi arac\u0131 ne zaman kullanaca\u011f\u0131na karar vermesi i\u00e7in, LLM'e mevcut ara\u00e7lar\u0131 ve kullan\u0131mlar\u0131n\u0131 a\u00e7\u0131klayan bir talimat (prompt) g\u00f6ndermeliyiz.<\/p>\n
Ajan\u0131 \u00c7al\u0131\u015ft\u0131rma ve \u0130lk Etkile\u015fim<\/h4>\n
\u015eimdi t\u00fcm par\u00e7alar\u0131 bir araya getirelim. Ajan\u0131m\u0131z, kullan\u0131c\u0131 girdisini alacak, LLM'e soracak, LLM bir ara\u00e7 kullanmaya karar verirse o arac\u0131 \u00e7al\u0131\u015ft\u0131racak ve nihai yan\u0131t\u0131 kullan\u0131c\u0131ya d\u00f6nd\u00fcrecektir.<\/p>\n
\ndef run_agent(user_input, memory, available_tools):\n # LLM'e ara\u00e7lar\u0131 tan\u0131tmak i\u00e7in bir sistem mesaj\u0131\n tool_descriptions = \"\\n\".join([\n f\"- {name}: {func.__doc__ if func.__doc__ else 'A\u00e7\u0131klama yok.'}\"\n for name, func in available_tools.items()\n ])\n system_prompt = f\"\"\"Sen yard\u0131msever bir yapay zeka asistan\u0131s\u0131n.\n A\u015fa\u011f\u0131daki ara\u00e7lar\u0131 kullanabilirsin:\n {tool_descriptions}\n \n E\u011fer bir ara\u00e7 kullanman gerekiyorsa, 'TOOL_CALL:arac_adi:arg\u00fcman' format\u0131nda yan\u0131t ver.\n \u00d6rnek: 'TOOL_CALL:web_search:\u0130stanbul'un n\u00fcfusu'\n E\u011fer bir araca ihtiyac\u0131n yoksa veya arac\u0131 kulland\u0131ktan sonra nihai yan\u0131t\u0131 vereceksen do\u011frudan yan\u0131t\u0131n\u0131 yaz.\"\"\"\n\n messages = [{\"role\": \"system\", \"content\": system_prompt}]\n messages.extend(memory.get_history())\n messages.append({\"role\": \"user\", \"content\": user_input})\n\n try:\n response = client.chat.completions.create(\n model=\"gpt-3.5-turbo\",\n messages=messages,\n temperature=0.7,\n max_tokens=250\n )\n ai_response_content = response.choices[0].message.content.strip()\n \n # E\u011fer LLM bir ara\u00e7 \u00e7a\u011fr\u0131s\u0131 yapt\u0131ysa\n if ai_response_content.startswith(\"TOOL_CALL:\"):\n parts = ai_response_content.split(\":\", 2)\n tool_name = parts[1]\n tool_arg = parts[2] if len(parts) > 2 else \"\"\n\n if tool_name in available_tools:\n print(f\"Ajan arac\u0131 kullan\u0131yor: {tool_name}('{tool_arg}')\")\n tool_output = available_tools[tool_name](tool_arg)\n print(f\"Ara\u00e7 \u00e7\u0131kt\u0131s\u0131: {tool_output[:100]}...\") # \u00c7\u0131kt\u0131y\u0131 k\u0131salt\n\n # Ara\u00e7 \u00e7\u0131kt\u0131s\u0131n\u0131 LLM'e geri besle\n tool_feedback_prompt = f\"Kullan\u0131c\u0131 '{user_input}' dedi. Sen '{tool_name}' arac\u0131n\u0131 '{tool_arg}' ile \u00e7a\u011f\u0131rd\u0131n. Ara\u00e7tan gelen \u00e7\u0131kt\u0131: '{tool_output}'. \u015eimdi bu bilgiye dayanarak kullan\u0131c\u0131ya yan\u0131t ver.\"\n final_response = llm_query_with_memory(tool_feedback_prompt, memory)\n return final_response\n else:\n return f\"Bilinmeyen ara\u00e7 \u00e7a\u011fr\u0131s\u0131: {tool_name}\"\n else:\n # Do\u011frudan yan\u0131t verdiyse\n memory.add_interaction(user_input, ai_response_content)\n return ai_response_content\n\n except Exception as e:\n return f\"Ajan \u00e7al\u0131\u015f\u0131rken bir hata olu\u015ftu: {e}\"\n\n# Ajan\u0131 \u00e7al\u0131\u015ft\u0131ral\u0131m\n# my_memory = SimpleMemory()\n# print(\"Ajan: Merhaba, size nas\u0131l yard\u0131mc\u0131 olabilirim?\")\n# while True:\n# user_input = input(\"Siz: \")\n# if user_input.lower() == \"\u00e7\u0131k\u0131\u015f\":\n# break\n# agent_response = run_agent(user_input, my_memory, tools)\n# print(f\"Ajan: {agent_response}\")\n<\/pre>\n<\/code><\/p>\n
Bu kod, basit bir AI ajan\u0131 d\u00f6ng\u00fcs\u00fcn\u00fc sim\u00fcle eder. Ger\u00e7ek LangChain veya LlamaIndex ajanlar\u0131 \u00e7ok daha sofistike karar verme ve ara\u00e7 y\u00f6netimi mekanizmalar\u0131na sahiptir, ancak bu \u00f6rnek temel prensipleri anlamak i\u00e7in yeterlidir.<\/p>\n
Test Etme ve \u0130yile\u015ftirme<\/h3>\n
Ajan\u0131n\u0131z\u0131 geli\u015ftirdikten sonra, beklendi\u011fi gibi \u00e7al\u0131\u015ft\u0131\u011f\u0131ndan emin olmak ve performans\u0131n\u0131 art\u0131rmak i\u00e7in test etmek ve iyile\u015ftirmek hayati \u00f6neme sahiptir.<\/p>\n
Test Senaryolar\u0131 Olu\u015fturma<\/h4>\n
Ajan\u0131n\u0131z\u0131n farkl\u0131 durumlarla nas\u0131l ba\u015fa \u00e7\u0131kt\u0131\u011f\u0131n\u0131 g\u00f6rmek i\u00e7in \u00e7e\u015fitli test senaryolar\u0131 haz\u0131rlay\u0131n:<\/p>\n
\n- Beklenen Senaryolar:<\/strong> Ajan\u0131n tasarlanma amac\u0131 olan g\u00f6revleri do\u011fru bir \u015fekilde yerine getirip getirmedi\u011fini kontrol edin.<\/li>\n
- Kenar Durumlar:<\/strong> Belirsiz veya eksik girdilerle nas\u0131l tepki verdi\u011fini test edin.<\/li>\n
- Hata Senaryolar\u0131:<\/strong> Ara\u00e7lar\u0131n ba\u015far\u0131s\u0131z oldu\u011fu veya API'lerin yan\u0131t vermedi\u011fi durumlarda ajan\u0131n ne yapt\u0131\u011f\u0131n\u0131 g\u00f6zlemleyin.<\/li>\n
- Bellek Testleri:<\/strong> Ge\u00e7mi\u015f konu\u015fmalar\u0131 do\u011fru hat\u0131rlay\u0131p hat\u0131rlamad\u0131\u011f\u0131n\u0131 ve ba\u011flam\u0131 koruyup korumad\u0131\u011f\u0131n\u0131 kontrol edin.<\/li>\n<\/ul>\n
Her senaryo i\u00e7in beklenen \u00e7\u0131kt\u0131y\u0131 \u00f6nceden belirlemek, testleri daha objektif hale getirir.<\/p>\n
Hata Ay\u0131klama ve Performans Analizi<\/h4>\n
Testler s\u0131ras\u0131nda kar\u015f\u0131la\u015ft\u0131\u011f\u0131n\u0131z hatalar\u0131 ve beklenmedik davran\u0131\u015flar\u0131 dikkatlice inceleyin. LLM'in verdi\u011fi kararlar\u0131 (\u00f6zellikle ara\u00e7 \u00e7a\u011fr\u0131lar\u0131n\u0131) anlamak i\u00e7in LLM'e g\u00f6nderilen prompt'lar\u0131 ve al\u0131nan yan\u0131tlar\u0131 loglay\u0131n. Ajan\u0131n yan\u0131t s\u00fcrelerini ve kaynak kullan\u0131m\u0131n\u0131 (CPU, bellek) izleyerek performans darbo\u011fazlar\u0131n\u0131 tespit edebilirsiniz.<\/p>\n
Prompt m\u00fchendisli\u011fi, LLM tabanl\u0131 ajanlar\u0131n performans\u0131n\u0131 art\u0131rman\u0131n \u00f6nemli bir yoludur. LLM'e verilen talimatlar\u0131 netle\u015ftirmek, \u00f6rnekler sunmak ve k\u0131s\u0131tlamalar belirtmek, daha do\u011fru ve istenen davran\u0131\u015flar\u0131 tetikleyebilir.<\/p>\n
\u0130teratif Geli\u015ftirme S\u00fcreci<\/h4>\n
AI ajan\u0131 geli\u015ftirme, do\u011frusal bir s\u00fcre\u00e7 de\u011fildir; iteratif bir d\u00f6ng\u00fcd\u00fcr. Testlerden elde etti\u011finiz geri bildirimlere dayanarak ajan\u0131n kodunu, prompt'lar\u0131n\u0131, ara\u00e7lar\u0131n\u0131 veya bellek mekanizmas\u0131n\u0131 s\u00fcrekli olarak iyile\u015ftirin. K\u00fc\u00e7\u00fck de\u011fi\u015fiklikler yap\u0131n, tekrar test edin ve sonu\u00e7lar\u0131 de\u011ferlendirin. Bu s\u00fcrekli iyile\u015ftirme d\u00f6ng\u00fcs\u00fc, ajan\u0131n zamanla daha yetenekli ve g\u00fcvenilir hale gelmesini sa\u011flayacakt\u0131r.<\/p>\n
Gelecek Ad\u0131mlar ve \u0130leri Konular<\/h3>\n
\u0130lk AI ajan\u0131nz\u0131 ba\u015far\u0131yla olu\u015fturduktan sonra, onu daha da geli\u015ftirmek ve yeni ufuklara ta\u015f\u0131mak i\u00e7in bir\u00e7ok ileri konu ve ad\u0131m bulunmaktad\u0131r.<\/p>\n
Ajan\u0131 Da\u011f\u0131tma (Deployment)<\/h4>\n
Ajan\u0131n\u0131z yerel makinenizde \u00e7al\u0131\u015f\u0131rken, onu ba\u015fkalar\u0131n\u0131n da kullanabilece\u011fi bir platforma ta\u015f\u0131mak (da\u011f\u0131tmak) \u00f6nemlidir. Bu genellikle bir web uygulamas\u0131 (Flask, Django, Streamlit), bir bulut fonksiyonu (AWS Lambda, Google Cloud Functions) veya bir Docker konteyneri arac\u0131l\u0131\u011f\u0131yla yap\u0131l\u0131r. Da\u011f\u0131t\u0131m s\u0131ras\u0131nda g\u00fcvenlik, \u00f6l\u00e7eklenebilirlik ve maliyet gibi fakt\u00f6rleri g\u00f6z \u00f6n\u00fcnde bulundurmal\u0131s\u0131n\u0131z. API anahtarlar\u0131n\u0131z\u0131 asla do\u011frudan kod i\u00e7ine g\u00f6mmeyin, ortam de\u011fi\u015fkenleri veya g\u00fcvenli s\u0131r y\u00f6netim sistemleri kullan\u0131n.<\/p>\n
Geli\u015fmi\u015f Ajan Mimarileri<\/h4>\n
Basit bir \"alg\u0131la-d\u00fc\u015f\u00fcn-hareket et\" d\u00f6ng\u00fcs\u00fcnden \u00f6teye ge\u00e7mek isterseniz, daha geli\u015fmi\u015f ajan mimarilerini ke\u015ffedebilirsiniz:<\/p>\n
\n- Planlama Tabanl\u0131 Ajanlar:<\/strong> Daha uzun vadeli hedefler i\u00e7in ad\u0131m ad\u0131m planlar olu\u015fturabilen ajanlar.<\/li>\n
- Hiyerar\u015fik Ajanlar:<\/strong> Ana bir ajan\u0131n alt g\u00f6revleri daha k\u00fc\u00e7\u00fck, uzmanla\u015fm\u0131\u015f ajanlara delege etti\u011fi yap\u0131lar.<\/li>\n
- \u00c7oklu Ajan Sistemleri:<\/strong> Birden fazla ajan\u0131n i\u015fbirli\u011fi yaparak karma\u015f\u0131k problemleri \u00e7\u00f6zd\u00fc\u011f\u00fc sistemler.<\/li>\n
- Otonom Ajanlar (AutoGPT, AgentGPT):<\/strong> Kendi ba\u015flar\u0131na g\u00f6revleri b\u00f6l\u00fcp, planlay\u0131p ve y\u00fcr\u00fctebilen ajanlar.<\/li>\n<\/ul>\n
LangChain ve LlamaIndex gibi framework'ler bu t\u00fcr geli\u015fmi\u015f mimarileri desteklemek i\u00e7in zengin \u00f6zellikler sunar.<\/p>\n
Etik ve G\u00fcvenlik Konular\u0131<\/h4>\n
AI ajanlar\u0131 geli\u015ftirirken etik ve g\u00fcvenlik sorumluluklar\u0131n\u0131 g\u00f6z ard\u0131 etmemek gerekir:<\/p>\n
\n- Yanl\u0131l\u0131k (Bias):<\/strong> Ajan\u0131n kulland\u0131\u011f\u0131 verilerdeki veya LLM'deki yanl\u0131l\u0131klar\u0131n fark\u0131nda olun ve bunlar\u0131 azaltmaya \u00e7al\u0131\u015f\u0131n.<\/li>\n
- \u015eeffafl\u0131k:<\/strong> Ajan\u0131n kararlar\u0131n\u0131n ve eylemlerinin anla\u015f\u0131labilir olmas\u0131n\u0131 sa\u011flamaya \u00e7al\u0131\u015f\u0131n.<\/li>\n
- Gizlilik:<\/strong> Hassas verileri i\u015flerken kullan\u0131c\u0131 gizlili\u011fini koruyun ve veri g\u00fcvenli\u011fi standartlar\u0131na uyun.<\/li>\n
- K\u00f6t\u00fcye Kullan\u0131m:<\/strong> Ajan\u0131n\u0131z\u0131n k\u00f6t\u00fc niyetli ama\u00e7lar i\u00e7in kullan\u0131labilece\u011fi potansiyel riskleri de\u011ferlendirin ve \u00f6nlemler al\u0131n.<\/li>\n<\/ul>\n
Sorumlu yapay zeka geli\u015ftirme prensiplerine uymak, hem projenizin ba\u015far\u0131s\u0131 hem de toplum i\u00e7in \u00f6nemlidir.<\/p>\n
Sonu\u00e7<\/h3>\n
\u0130lk yapay zeka ajan\u0131nz\u0131 olu\u015fturmak, karma\u015f\u0131k g\u00f6r\u00fcnen bu alan\u0131 ke\u015ffetmek i\u00e7in heyecan verici ve \u00f6d\u00fcllendirici bir yolculuktur. Bu makalede ele ald\u0131\u011f\u0131m\u0131z ad\u0131mlar\u0131 takip ederek, bir ajan\u0131n temel bile\u015fenlerini anlayacak, onu tasarlayacak, kodlayacak, test edecek ve gelecekteki geli\u015ftirmeler i\u00e7in sa\u011flam bir temel olu\u015fturacaks\u0131n\u0131z. Unutmay\u0131n, en iyi \u00f6\u011frenme, pratik yaparak ve deneme yan\u0131lma yoluyla ger\u00e7ekle\u015fir. K\u00fc\u00e7\u00fck ba\u015flay\u0131n, s\u00fcrekli \u00f6\u011frenin ve yarat\u0131c\u0131l\u0131\u011f\u0131n\u0131z\u0131 kullanmaktan \u00e7ekinmeyin. Yapay zeka ajanlar\u0131 d\u00fcnyas\u0131, ke\u015ffedilmeyi bekleyen sonsuz potansiyellerle dolu!<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\n\n- AI ajan\u0131 ile chatbot aras\u0131ndaki fark nedir?<\/strong><\/dt>\n
- Chatbot'lar genellikle belirli bir senaryo veya konu\u015fma ak\u0131\u015f\u0131 i\u00e7inde kullan\u0131c\u0131yla etkile\u015fim kurmak \u00fczere tasarlanm\u0131\u015fken, AI ajanlar\u0131 daha geni\u015f bir otonomiye sahiptir. \u00c7evrelerini<\/p>\n","protected":false},"excerpt":{"rendered":"Yapay zeka ajanlar\u0131, \u00e7evrelerini alg\u0131layabilen, bu alg\u0131lara dayanarak ak\u0131l y\u00fcr\u00fctebilen ve belirli hedeflere ula\u015fmak i\u00e7in eylemlerde bulunabilen oton…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-38985","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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