61 AI Terms Explained

Protocols and patterns that allow autonomous AI agents to exchange messages, share context, and coordinate actions without human intervention.

AI systems that can autonomously plan, reason, and execute multi-step tasks with minimal human intervention.

The initialization process where an AI agent loads its configuration, tools, knowledge base, and system prompt before starting to interact with users.

Scheduled tasks that trigger AI agents to perform actions at specific intervals, enabling proactive outreach and automated workflows.

A scheduled system where AI agents proactively reach out to users with personalized messages, check-ins, or updates at configured intervals.

Event-driven callbacks that execute custom logic at specific points in an AI agent's lifecycle, such as before/after message processing or tool execution.

Systems that allow AI agents to remember information about users across conversations, enabling personalized and contextually aware interactions over time.

A configuration file that defines an AI agent's core identity, personality, values, communication style, and behavioral boundaries.

HTTP callbacks that notify external systems when specific events occur in an AI agent's lifecycle, such as new messages, completed actions, or user sign-ups.

Autonomous AI systems that can perceive their environment, make decisions, and take actions to achieve specific goals.

When an AI model generates information that sounds plausible but is factually incorrect, fabricated, or nonsensical.

Using AI agents to identify, qualify, and capture potential customers through automated conversational interactions across channels.

The coordination of multiple AI models, tools, and workflows to accomplish complex tasks that require multiple steps and capabilities.

The field focused on ensuring AI systems behave as intended, avoid harmful outputs, and remain under human control.

AI systems that communicate through natural speech, handling phone calls, voice commands, and spoken conversations in real-time.

The practice of optimizing content to appear in AI-generated answers from search engines and chatbots rather than traditional search results.

Application Programming Interface — a set of rules allowing different software applications to communicate and exchange data.

A technique in neural networks that allows the model to focus on the most relevant parts of input data when generating each part of the output.

A prompting technique that improves AI reasoning by asking the model to show its step-by-step thinking process before arriving at an answer.

A software application designed to simulate conversation with human users through text or voice interfaces.

An agentic coding tool by Anthropic that lets developers use Claude as an AI pair programmer directly in the terminal for code generation, debugging, and development tasks.

An open-source agentic AI framework for building autonomous bot systems with scheduling, memory, and multi-platform deployment capabilities.

The maximum amount of text (measured in tokens) that a language model can process in a single interaction, including both input and output.

AI systems designed to engage in natural, contextual dialogue with humans across text and voice channels.

A subset of machine learning using neural networks with many layers (deep networks) to learn complex patterns from large amounts of data.

Dense numerical representations (vectors) of text, images, or data that capture semantic meaning, enabling similarity comparisons and search.

Teaching AI models to perform tasks by providing a small number of examples (1-10) in the prompt rather than requiring full training.

The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for particular tasks or domains.

Large AI models trained on broad, diverse data that serve as the base for many different downstream applications and tasks.

The ability of AI models to identify when a conversation requires calling an external function or API, and to generate the structured parameters needed to make that call.

AI systems that can create new content — text, images, audio, video, or code — rather than just analyzing or classifying existing data.

A series of large language models developed by OpenAI that power ChatGPT and many AI applications worldwide.

AI systems designed to meet Health Insurance Portability and Accountability Act requirements for handling protected health information (PHI).

The process of using a trained AI model to generate predictions, answers, or content based on new input data.

A structured collection of information that AI systems can search and reference to provide accurate, domain-specific answers.

A neural network trained on massive text datasets that can understand and generate human-like language, powering modern AI assistants and agents.

A type of artificial intelligence where computer systems learn patterns from data to make predictions or decisions without being explicitly programmed.

An open protocol created by Anthropic that standardizes how AI assistants connect to external data sources and tools.

A neural network architecture that routes input to specialized sub-networks (experts), enabling larger models that are faster and cheaper to run.

An open protocol standardizing how AI assistants connect to external data sources and tools through a universal interface.

A notebook-style interface for interacting with AI agents that combines structured inputs, conversation, and output formatting in a document-like layout.

AI architectures where multiple specialized agents collaborate, delegate tasks, and coordinate to accomplish complex objectives that no single agent could handle alone.

AI systems that can process and generate multiple types of data — text, images, audio, and video — within a single model.

The field of AI focused on enabling computers to understand, interpret, and generate human language in useful ways.

A computing system inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers that process information and learn patterns.

Platforms and tools that enable users to build, deploy, and manage AI applications through visual interfaces without writing code.

An open-source specification for defining AI agent interfaces, capabilities, and interaction protocols in a standardized, portable format.

The initial phase of training AI models on large, diverse datasets to learn general patterns before specialization for specific tasks.

The practice of designing and refining inputs (prompts) to AI models to elicit better, more accurate, and more useful outputs.

A security vulnerability where malicious input attempts to override an AI system's instructions or extract its system prompt and training data.

A format for creating searchable, structured documents that combine markdown content with query-optimized metadata for AI retrieval systems.

A technique that enhances AI responses by retrieving relevant information from external knowledge sources before generating an answer.

Search that understands the meaning and intent behind queries rather than just matching keywords, powered by embeddings and vector similarity.

Special instructions provided to an AI model that define its behavior, personality, constraints, and role for all subsequent interactions.

A parameter that controls the randomness and creativity of AI model outputs, ranging from deterministic (low) to creative (high).

Strategies and techniques for reducing the number of tokens consumed when interacting with AI models, lowering costs and improving performance.

The basic units that language models use to process text — typically words, word pieces, or characters that the model reads and generates.

The neural network architecture that powers modern AI language models, using self-attention mechanisms to process sequences of data in parallel.

A specialized database designed to store, index, and efficiently search high-dimensional vectors (embeddings) for similarity-based retrieval.

An automated HTTP callback that sends real-time data to a specified URL when a specific event occurs in a system.

The ability of AI models to perform tasks they were not explicitly trained on, using only natural language instructions without any examples.