Will Artificial Intelligence take our jobs? My Thesis Process The awareness that we need to integrate artificial intelligence into our projects in some way The desire and curiosity to create a chatbot in the .NET ecosystem
we are. We all know Chatbots. They are amazing conversationalists. You ask a question, and they give an answer based on what they learned during training.
in. If Chatbots are the 'Brain', Agents are the 'Brain' plus 'Hands'. An Agent is an AI system that can reason (think), plan, and most importantly, use tools. It doesn't just talk; it acts. It can read files. It can call APIs (like GitHub). It can query databases (like MongoDB). It can create files on your desktop. IT BREAKS OUT OF THE CHAT WİNDOW AND IT BREAKS OUT OF THE CHAT WİNDOW AND İNTERACTS WİTH THE REAL WORLD." İNTERACTS WİTH THE REAL WORLD." IT BREAKS OUT OF THE CHAT WİNDOW AND İNTERACTS WİTH THE REAL WORLD."
a Loop. Observe: It looks at the user's request. Reason: It thinks, 'What do I need to solve this?' Act: It selects the right tool (Plugin) and uses it. Reflect: It looks at the result. Is it enough? If not, it loops back and tries another tool. A Chatbot follows a simple linear path: Input -> Processing -> Output.
Another sends invitations, Another manages the guest list, Another sets up the seating arrangement. In a wedding organization, there are many tasks such as venue reservation, guest list, meal plan, and seating arrangement. Each of these tasks is carried out by a “person in charge.” Similarly, in an Agentic AI system, each task can be handled by a different artificial intelligence agent: Multi-agent workflow
me, the more I learn and understand. So be careful what you say. I can imitate you. But I can't answer something you haven't taught me. For example, if you ask me for my Turkish ID number, I won't know it LLM: The more information you give me, the more I learn and understand. That's why I say pay attention to the information you give me. I can imitate you, but I can't answer something you haven't taught me. For example, if you ask me for my Turkish ID number, I wouldn't know it either :)
IT İS NOT POSSİBLE TO COMPLETELY ELİMİNATE HALLUCİNATİONS, IT İS NOT POSSİBLE TO COMPLETELY ELİMİNATE HALLUCİNATİONS, BECAUSE THEY ARE A STATİSTİCAL BYPRODUCT OF LEARNİNG. BECAUSE THEY ARE A STATİSTİCAL BYPRODUCT OF LEARNİNG. HOWEVER, İT İS POSSİBLE TO MANAGE THEM AND CREATE THE HOWEVER, İT İS POSSİBLE TO MANAGE THEM AND CREATE THE RİGHT İNCENTİVES. RİGHT İNCENTİVES. IT İS NOT POSSİBLE TO COMPLETELY ELİMİNATE HALLUCİNATİONS, BECAUSE THEY ARE A STATİSTİCAL BYPRODUCT OF LEARNİNG. HOWEVER, İT İS POSSİBLE TO MANAGE THEM AND CREATE THE RİGHT İNCENTİVES.
text as vectors, enabling fast and efficient searching of this data. Text and user queries are converted into vectors, and the most similar vectors are identified to deliver the most semantically relevant results. This structure facilitates finding similar content within large datasets.
ONE place. No synchronization headaches. Seamless integration with .NET. WHEREVER YOUR NORMAL DATA İS STORED, STORE YOUR VECTORS THERE TOO. WHEREVER YOUR NORMAL DATA İS STORED, STORE YOUR VECTORS THERE TOO.
vs. Embeddings in a separate Vector DB. The Scenario: Vector DB finds a perfect match ("Job ID #105"). The Reality: That job was deleted from SQL DB 5 minutes ago. The Result: "Record Not Found" error. Disconnected brain and memory. Our Solution: Unified storage in MongoDB Atlas (No sync required).
It? If you're building a simple “Chat” application. If you just need to summarize a text and move on. If you don't need orchestration or memory. .NET AI Ecosystem: OpenAI .NET API
to Use It? If you want to make the LLM talk using your own code, database, and APIs. If you want to do RAG. If you want a single “Agent” (Career Architect) to have different capabilities (Plugins). Application Framework .NET AI Ecosystem: Semantic Kernel (The Orchestrator)
Toolbox 🧰 + 🧠. (Plugins, Memory, RAG) Which should we use ? OpenAI .NET API: This is the most basic level. It's great if you're just building a simple chatbot. But you have to manually write the memory, RAG, and tool usage (Tool Call). It's like driving a car with a manual transmission. Microsoft Semantic Kernel: This is the technology we used in today's demo. Why? Because we built a single “Super Agent.” We wanted this agent to have memory (MongoDB), eyes (GitHub API), and hands (file system). The Semantic Kernel is the nervous system connecting these organs to the brain.
GitHubPlugin ile profili okur. Ajan, eksik yetenekleri belirler. RoadmapPlugin ile DB'den kaynak arar. CreateRoadmapPng ile görsel yol haritasını çizer ve kaydeder. Ve rapor çıkartır
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