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AI gateways act as hubs that transform fragmented technologies—like legacy systems, AI models, and siloed data repositories— into cohesive, functional ecosystems. Instead of systems operating in isolation, gateways ensure they interact smoothly and efficiently.

An autonomous agent is an AI-powered system capable of making decisions and performing actions independently to achieve specific goals. They gather real-time data, evaluate possible actions based on programmed rules or learning models, and execute decisions to adapt to dynamic environments.

Feature vectors are the numerical fingerprints of data, transforming raw information into structured representations that algorithms can analyze, compare, and learn from. By encoding the attributes and relationships of data into numerical values, feature vectors allow AI systems to identify patterns, classify data points, and make predictions with precision.

LLM agents are autonomous extensions of large language models (LLMs), capable of interpreting complex instructions and executing tasks without human intervention. Unlike static models, LLM agents integrate generative capabilities with task-specific logic to dynamically adapt to changing requirements.

LLM inference is the process of applying a trained Large Language Model to generate meaningful outputs from new inputs in real time. It’s the operational phase where an LLM transforms its learned knowledge—gathered during training—into actionable results, whether by answering questions, synthesizing data, or automating workflows.

Multi-Agent AI (MAAI) is a system where multiple autonomous AI agents collaborate in real-time to solve complex problems. By dividing tasks and sharing information, these agents create scalable, flexible, and efficient solutions that adapt dynamically to changing environments.

Operational AI refers to a form of artificial intelligence designed to process data and take actions instantly. Unlike traditional AI systems, which analyze past data to provide insights, Operational AI works in dynamic, ever-changing environments. It doesn’t just suggest what might happen—it decides and acts in the moment.

Semantic caching is an advanced data retrieval mechanism that prioritizes meaning and intent over exact matches. By breaking down queries into reusable, context-driven fragments, semantic caching allows systems to respond faster and with greater accuracy.

A Vector DB is a specialized database designed to store and query embeddings, which are numerical representations of unstructured data like text, images, or audio. This allows AI systems to retrieve data based on meaning and relationships rather than exact matches.

A vector store is a specialized database designed to organize and retrieve feature vectors—numerical representations of data like text, images, or audio. These stores are essential in AI and machine learning workflows, enabling high-speed searches, efficient comparisons, and pattern recognition across vast datasets.