A knowledge base is a centralized repository where organizations store, organize, and retrieve structured information that AI agents and human teams use to make decisions, answer questions, and complete tasks.

Output filtering for safety refers to the process of screening, validating, and sanitizing responses generated by AI agents before those responses reach end users or downstream systems.

Storage options refer to the various mechanisms that AI agent systems use to persist, retrieve, and manage data across sessions, tasks, and workflows.

Task-centric structure refers to an architectural pattern in AI agent systems where the fundamental unit of organization is the discrete task rather than the conversational thread, user session, or data entity.

The agent data layer is the foundational infrastructure that enables AI agents to store, retrieve, and manage information across sessions and workflows.

Agent memorization refers to the practice of caching the results of expensive computations, tool calls, or reasoning steps so that an AI agent can reuse them in future interactions without repeating the same work.

Agent memory refers to the mechanisms that allow AI agents to store, retrieve, and use information across interactions and sessions. Without memory, an agent treats every conversation as a blank slate; it cannot learn from past exchanges, recall user preferences, or build context over time.

A context window is the maximum amount of text that a large language model can process in a single interaction, measured in tokens.

Document chunking is the process of breaking large documents into smaller, semantically meaningful segments that AI systems can process, embed, and retrieve efficiently.

Function calling enables large language models to invoke external tools, APIs, and code by generating structured outputs that match predefined schemas.

Graph state coordination refers to the mechanisms and protocols that manage shared state across multiple nodes in a distributed agent system structured as a directed graph.

Inference optimization refers to the techniques and strategies used to make machine learning model predictions faster, cheaper, and more efficient without significantly degrading accuracy.

Memory architecture refers to the structured design of how an AI agent stores, retrieves, and organizes information across sessions and interactions.

Prompt injection is an attack technique where malicious input manipulates a large language model into ignoring its original instructions and executing unintended actions.

A RAG pipeline, short for Retrieval Augmented Generation pipeline, is an architecture that combines information retrieval with large language model generation to produce accurate, grounded responses.

Retrieval Augmented Generation, commonly known as RAG, is an architectural pattern that enhances large language models by retrieving relevant information from external knowledge sources before generating a response.

The Saga Pattern is a design approach for managing distributed transactions across multiple services by breaking them into a sequence of local transactions, each with a corresponding compensating action if something fails.

Semantic search is a retrieval method that finds information based on the meaning and intent behind a query rather than matching exact keywords.

Sequential execution refers to the processing of tasks or instructions in a strict, ordered sequence where each step must complete before the next one begins.

Session memory refers to the temporary storage of conversational context that an AI agent maintains during a single interaction or defined time window.

A supervisor agent is a specialized AI agent that orchestrates, monitors, and coordinates the actions of multiple subordinate agents working toward a shared objective.

A task-driven agent is an AI system designed to accomplish specific, well-defined objectives through autonomous reasoning and action.

A task queue is a data structure that holds pending work items in an ordered sequence, allowing systems to process jobs asynchronously rather than immediately upon receipt.

Tool calling is the mechanism by which an AI agent invokes external functions, APIs, or services to perform actions beyond text generation.

A unified tool interface is a standardized abstraction layer that enables AI agents to interact with diverse external tools, APIs, and services through a single, consistent protocol.

Workflow automation refers to the use of software to execute repeatable business processes with minimal human intervention.

Workflow dependencies define the relationships between tasks in an automated process, specifying which steps must complete before others can begin.

Workflow orchestration refers to the automated coordination, sequencing, and management of tasks across multiple systems, services, or agents to accomplish a defined business objective.

Document authenticity and forgery detection is the process of verifying that identity documents, financial records and legal papers are genuine rather than fabricated, altered or stolen.

Merchant onboarding automation uses AI agents, workflow orchestration and integrated data systems to bring new merchants onto payment platforms with minimal manual intervention.

A Registry Lookup Agent is an AI agent that queries external registries, databases and official sources to verify information about businesses, individuals or assets.

Reputation signal aggregation is the process of collecting, normalizing and synthesizing trust indicators from multiple sources to assess the credibility of an entity.