AI Agents in GTM: A Glossary

Covered in this glossary: Agentic AI, Agentic AI in CRM, Agentic Workflow, Agent Guardrails, Least Privilege, Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), Tool Use in AI Agents, and LLM-as-a-Judge.

AGENTIC AI. Agentic AI is software that plans and executes multi-step tasks on its own, taking actions in real systems rather than only generating text. Where a chatbot answers and a copilot suggests, an agent decides what to do next based on what it finds and then does it, such as updating a CRM record, sending an email, or opening a ticket. (Source: Anthropic, framework for safe and trustworthy agents.)

AGENTIC AI IN CRM. Agentic AI in a CRM is software that takes actions on your CRM data rather than only answering questions about it: enriching records, routing leads, updating fields after a call, merging duplicates, and flagging renewal risk. That autonomy separates it from a copilot, which suggests while a human performs every write. (Source: Mindlyft, agentic AI in CRM: the full guide.)

AGENTIC WORKFLOW. An agentic workflow is a process in which one or more AI agents plan and carry out a multi-step task with some autonomy, deciding which steps to take and calling tools or systems to complete them, rather than following a fixed script. A human typically sets the goal and reviews or approves the key steps.

AGENT GUARDRAILS. Agent Guardrails are predefined boundaries or rules set for AI-driven sales agents to prevent errors, ensure compliance, and align with business goals. They act as checkpoints to stop agents from taking actions that could harm customer relationships or violate company policies. (Source: Mindlyft, automate post-call CRM updates.)

LEAST PRIVILEGE. Least privilege is the principle of granting an identity only the permissions its job actually requires, and no more. For AI agents it is a primary defense: an agent scoped to read contacts and log activities simply cannot delete records or touch billing, so a wrong or hijacked action is bounded by what it was never allowed to do. (Source: OWASP, Excessive Agency, LLM06:2025.)

MODEL CONTEXT PROTOCOL (MCP). The Model Context Protocol is an open standard, introduced by Anthropic, for connecting AI assistants and agents to external tools, data sources, and systems through a common interface. Instead of building a custom integration for each app, developers expose their data and actions through an MCP server that any MCP-compatible AI client can call. (Source: Model Context Protocol, official site.)

RETRIEVAL-AUGMENTED GENERATION (RAG). Retrieval-Augmented Generation is a technique where an AI system first retrieves relevant documents or data from an external source, then feeds that context to a language model so its answer is grounded in that information rather than the model's memory alone. It is used to make responses more accurate and up to date.

TOOL USE IN AI AGENTS. Tool Use in AI Agents refers to the ability of AI systems to interact with external systems, data sources, or applications to perform tasks automatically. This capability enables AI agents to handle complex workflows by leveraging pre-existing tools, reducing manual effort and improving operational efficiency in B2B environments. (Source: Mindlyft, automate post-call CRM updates.)

LLM-AS-A-JUDGE. LLM-as-a-Judge refers to using a large language model to evaluate and prioritize sales opportunities or customer interactions by analyzing data and making recommendations. It acts as a decision-making tool to streamline processes but requires human oversight for final actions. (Source: Mindlyft, automate post-call CRM updates.)