Engineering

Introduction In the last few years, the term agentic AI has moved from academic footnote to a central pillar of the industry’s roadmap. While “agentic” simply describes systems that can act autonomously toward a goal—selecting tools, planning, and iterating on their own—its practical realization has sparked a wave of new products, research directions, and engineering challenges. Few figures have shaped this shift as visibly as Sam Altman, CEO of OpenAI, whose public pronouncements, internal memos, and product launches have provided a de‑facto playbook for building and deploying agentic systems at scale. ...

Introduction The rapid rise of large language models (LLMs)—GPT‑4, Claude, Llama 2, and their open‑source cousins—has shifted the bottleneck from model inference to information retrieval. When a model needs to answer a question, summarize a document, or generate code, it often benefits from grounding its output in external knowledge. This is where vector databases (or vector search engines) come into play: they store high‑dimensional embeddings and provide approximate nearest‑neighbor (ANN) search that can retrieve the most relevant pieces of information in milliseconds. ...