Agents & AI
How do agents use any of this safely?
How language models and agents consume governed lakehouse data, and what they need from the layers beneath.
18 reference entries
Agentic Analytics Agentic Analytics represents the next frontier in business intelligence and data engineering. Agentic Workflows An Agentic Workflow is a structured sequence of AI-driven steps in which one or more autonomous agents coordinate to accomplish a complex, multi-stage. AI Agents An AI Agent is an autonomous software system that uses a Large Language Model (LLM) as its core reasoning engine to perceive its environment, form plans. Autonomous Analytics Autonomous Analytics is the discipline and set of technologies that enable AI systems to independently discover, retrieve, analyze, and interpret enterprise. Context Window The context window of a Large Language Model is the total amount of text, measured in tokens, that the model can process and reason about simultaneously. Hallucination Mitigation Hallucination in Large Language Models refers to the generation of content that is factually incorrect, unverifiable, or entirely fabricated, but presented. Large Language Models (LLMs) A Large Language Model (LLM) is an artificial intelligence model trained on massive quantities of text data to understand, generate, and reason with human. Model Fine-Tuning Model fine-tuning is the process of taking a pre-trained Large Language Model and continuing its training on a curated, domain-specific dataset to adapt its. Multi-Agent Systems A Multi-Agent System (MAS) is an architecture in which multiple independent AI agents collaborate to accomplish complex tasks that exceed what any single. Observability (AI Systems) AI System Observability is the practice of instrumenting, collecting, and analyzing telemetry data from Large Language Model applications and agentic systems. Prompt Engineering Prompt Engineering is the discipline of designing, structuring, and optimizing the text inputs (prompts) provided to a Large Language Model to elicit. Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) is an AI architecture pattern that enhances a Large Language Model's responses by dynamically retrieving relevant. Semantic Search Semantic Search is a search methodology that understands the intent and contextual meaning behind a query rather than performing a literal word-for-word. Text Embeddings A text embedding is a numerical representation of a piece of text (a word, sentence, paragraph, or entire document) expressed as a dense vector of. Text-to-SQL Text-to-SQL is the task of automatically translating a natural language question or instruction into a valid SQL query that, when executed against the target. Tool Use (Function Calling) Tool Use, also called Function Calling, is the capability of modern Large Language Models to generate structured requests to execute predefined external. Vector Databases A Vector Database is a specialized database management system engineered to store, index, and efficiently query high-dimensional vector embeddings at scale. Vector Search Vector Search (also called semantic search or similarity search) is a retrieval technique that finds results based on the conceptual meaning and semantic.