Tech Dictionary

An open protocol for agent-to-agent communication that lets independent AI agents from different vendors discover each other and collaborate on tasks.

A US carrier registration standard for sending application-to-person SMS over standard 10-digit long codes, requiring brand and campaign registration to avoid filtering.

A step an automation performs after the trigger, such as creating a record, sending a message, or calling an API.

GoHighLevel's two-tier structure where the agency (top-level) account manages settings, snapshots, and billing across many client sub-accounts (locations), each an isolated CRM workspace.

An AI system that uses an LLM to decide and take actions in a loop, calling tools and reacting to results to accomplish a goal with minimal step-by-step human direction.

The software layer around a language model that turns it into a working agent: the loop that calls the model, the tools it may use, context and memory management, permissions, sandboxing and the handling of results.

An open format for packaging reusable agent expertise: a folder containing a SKILL.md file (name and description metadata plus instructions) and optional scripts, references and assets, which an agent loads only when a task calls for it. Anthropic published the standard and donated it to the Agentic AI Foundation in October 2026; Claude, Claude Code and many other agent platforms support it.

GoHighLevel's visual, node-based environment for flow-based AI agents, with knowledge-base and web-search tools, API and MCP tools, conditional routing, variables, testing and version control. HighLevel now recommends Managed Agents (formerly Super Agents) for creating new agents and positions Agent Studio for maintaining existing ones. It is pay-per-use and excluded from every AI Employee plan.

AI systems that pursue a goal by planning steps, calling tools and acting on the results with limited human direction, ranging from fixed LLM workflows with predefined code paths to agents that direct their own process.

A RAG approach where an agent actively decides when and how to retrieve, choosing sources, issuing multiple searches, and refining queries instead of doing a single fixed lookup.

An open convention for a plain Markdown file, usually at the repository root, that gives AI coding agents a project's setup commands, test steps, conventions and other working instructions; it is stewarded by the Agentic AI Foundation under the Linux Foundation.

A module that combines multiple separate items back into a single structure, such as one array, text string, or numeric total.