AI & LLM Engineering for .NET Architects

Case Study: Building an Autonomous AI Agent for Software Dev

1 Views Updated 5/4/2026

Case Study: The AI Developer Agent

The holy grail of AI: An agent that can take a Jira ticket, read the code, find the bug, write the fix, and open a Pull Request. Here is how you architect it in C#.

1. Stage 1: Discovery

The agent uses a **File System Plugin** to list files. It then uses a **Search Plugin** to find the relevant function mentioned in the Jira ticket. It reads the code and 'Reflects' on the logic to find the mistake.

2. Stage 2: Sandbox Execution

NEVER let an AI edit your real code directly. The agent creates a temporary **Docker Sandbox**. It writes the fix there and runs the unit tests. If the tests fail, the agent reads the error log, fixes the code, and tries again. It repeats this loop until all tests pass.

3. Stage 3: Human Review

The final output is not a pushed commit, but a **Pull Request**. The AI provides a detailed summary of its thought process, what it changed, and why. This allows a human senior dev to do a 30-second review and click 'Merge', increasing productivity by 10x.

4. Interview Mastery

Q: "Will AI agents replace software engineers?"

Architect Answer: "AI agents will replace **Toil**, not engineers. They will handle the repetitive CSS fixes, the boring boilerplate, and the simple bug patches. The Software Engineer of 2026 is an **Agent Manager**. Their job is to define the architecture, design the prompts, and review the agents' work. We are moving from 'Writing' code to 'Directing' code, and those who master these agentic patterns will be the most valuable architects in the market."

AI & LLM ENGINEERING COMPLETE.

You are now equipped to build the future. The world is your intelligent canvas.

AI & LLM Engineering for .NET Architects
1. AI Foundations & Prompt Engineering
The LLM Landscape: Transformers, Attention, and Tokens Advanced Prompt Engineering: Few-shot, Chain-of-Thought, and ReAct Prompt Versioning & Management in Production LLM Cost Estimation: Token accounting and budget strategies
2. Semantic Kernel & Integration
Introduction to Microsoft Semantic Kernel (SK) Skills & Plugins: Extending the LLM with native C# functions Planner & Orchestration: Automating complex multi-step AI tasks Connectors: Switching between OpenAI, Azure OpenAI, and HuggingFace
3. Vector Databases & RAG
The RAG Pattern: Solving the 'Static Knowledge' problem Embeddings Deep Dive: Converting text to math Vector DBs: Azure AI Search vs Pinecode vs Milvus Hybrid Search: Combining Keyword and Semantic search for accuracy
4. Advanced RAG Techniques
Document Chunking Strategies: Overlap, Slidewindow, and Semantic splitting Recursive Document Processing for massive knowledge bases Context Window Management: Summarization vs Truncation Citations & Grounding: Ensuring the AI doesn't hallucinate
5. AI Safety & Guardrails
Content Moderation: Azure AI Content Safety integration Prompt Injection: Defending against adversarial attacks Punitiveness & Bias: Evaluating and mitigating model behavior Self-Correction Patterns: Letting the AI check its own work
6. Small Language Models (SLMs) & Local AI
The rise of SLMs: Phi-3, Llama-3-8B, and Mistral Running AI Locally with ONNX and LocalLLM Quantization: Running 70B models on 16GB RAM Edge AI: Deploying models to local devices and private clouds
7. Multimodal & Agentic AI
Multimodal AI: Processing Images, PDFs, and Audio in C# Agentic Workflows: Multi-agent collaboration with AutoGen Function Calling: Letting the LLM use your SQL and API tools Memory Management: Ephemeral vs Long-term Semantic memory
8. FAANG AI Engineer Interview
Case Study: Designing a Global Enterprise AI Knowledge Assistant Case Study: Building an Autonomous AI Agent for Software Dev