Culture in AI agent teams refers to the emergent patterns of interaction, decision making, and shared values that develop when multiple autonomous agents collaborate within an organization.

Pipeline architecture refers to a system design pattern where data or tasks flow through a sequence of discrete processing stages, with each stage performing a specific transformation before passing output to the next.

A runnable chain is a sequence of modular, composable steps that process data through multiple operations in a defined order.

Team operation modes define how multiple AI agents coordinate, communicate, and execute tasks within a multi-agent system.

The Agent API Layer is an abstraction that sits between AI agents and external services, enabling agents to interact with databases, third party platforms, and internal systems through standardized interfaces.

Agent matching refers to the process of selecting the most appropriate AI agent or model to handle a specific user request, task, or workflow.

An agentic workflow is a sequence of tasks where an AI agent autonomously plans, executes, and adapts its actions to achieve a goal.

Data persistence refers to the ability of information to outlive the process or session that created it, remaining available for future access and use.

Distributed state management refers to the coordination and synchronization of application data across multiple nodes, servers, or services in a system.

Execution dependencies are the external systems, services, data sources, and runtime conditions that an AI agent requires to complete a task successfully.

A processing pipeline is a structured sequence of operations that transforms raw data into usable outputs by passing information through discrete, ordered stages.

Prompt chaining is an AI orchestration technique where the output of one language model call becomes the input for the next, creating a sequence of interconnected prompts that collectively accomplish complex tasks.

A workflow pipeline is a structured sequence of automated tasks that processes data or actions through defined stages to achieve a specific outcome.

Workflow state persistence refers to the ability of an AI agent or automated system to save, retrieve, and resume its operational context across sessions, restarts, or failures.