LLM-Ops

Table of Contents Introduction What Are Agentic Workflows? 2.1. Core Principles 2.2. Why “Agentic” Matters Today Multi‑Model Orchestration: The Missing Link 3.1. Common Orchestration Patterns 3.2. Key Players in the Landscape Designing an Agentic Pipeline 4.1. Defining the Task Graph 4.2. State Management & Memory 4.3. Error Handling & Guardrails Practical Example: Building a “Research‑Assist” Agent with LangChain & OpenAI Functions 5.1. Setup & Dependencies 5.2. Step‑by‑Step Code Walk‑through 5.3. Running & Observing the Pipeline Observability, Monitoring, and Logging Security, Compliance, and Data Governance Scaling Agentic Workflows in Production Best Practices Checklist Future Directions: Towards Self‑Optimizing Agents Conclusion Resources Introduction The AI renaissance that began with large language models (LLMs) is now entering a second wave—one where the orchestration of multiple models, tools, and data sources becomes the decisive factor for real‑world impact. While a single LLM can generate impressive text, most enterprise‑grade problems require a sequence of specialized steps: retrieval, transformation, reasoning, validation, and finally action. When each step is treated as an autonomous “agent” that can decide what to do next, we arrive at agentic workflows. ...

Introduction The past few years have witnessed a dramatic transformation in how developers, product teams, and researchers interact with large language models (LLMs). In 2023–2024, prompt engineering—the art of crafting textual inputs that coax LLMs into producing the desired output—was the dominant paradigm. By 2026, however, the conversation has shifted toward agentic workflow orchestration: a higher‑level approach that treats LLMs as autonomous agents capable of planning, executing, and iterating on complex tasks across multiple tools and data sources. ...

Table of Contents Introduction Fundamentals of Retrieval‑Augmented Generation (RAG) 2.1. Why Retrieval Matters 2.2. Typical RAG Architecture Vector Databases: The Backbone of Modern Retrieval 3.1. Core Concepts 3.2. Popular Open‑Source & Managed Options Designing a Scalable RAG Pipeline 4.1. Data Ingestion & Embedding Generation 4.2. Indexing Strategies for Large Corpora 4.3. Query Flow & Latency Budgets Advanced Semantic Routing Strategies 5.1. Routing by Domain / Topic 5️⃣. Hierarchical Retrieval & Multi‑Stage Reranking 5.3. Contextual Prompt Routing 5.4. Dynamic Routing with Reinforcement Learning Practical Implementation Walk‑through 6.1. Environment Setup 6.2. Embedding Generation with OpenAI & Sentence‑Transformers 6.3. Storing Vectors in Milvus (open‑source) and Pinecone (managed) 6.4. Semantic Router in Python using LangChain 6.5. End‑to‑End Query Example Performance, Monitoring, & Observability Security, Privacy, & Compliance Considerations Future Directions & Emerging Research Conclusion Resources Introduction Retrieval‑Augmented Generation (RAG) has emerged as a practical paradigm for marrying the creativity of large language models (LLMs) with the factual grounding of external knowledge sources. While the academic literature often showcases elegant one‑off prototypes, real‑world deployments demand scalable, low‑latency, and maintainable pipelines. The linchpin of such systems is a vector database—a purpose‑built store for high‑dimensional embeddings—paired with semantic routing that directs each query to the most appropriate subset of knowledge. ...