AI Glossary
without bullshit

An AI agent is software that can autonomously observe its environment, make decisions, and perform actions to achieve a predefined goal — without waiting for human input at each step. An AI agent can, for example, sort incoming customer inquiries, update a CRM, and send a draft message — all in one coherent process.

An AI control center is the operating system for an AI workforce. It provides one place to define goals, budgets, approval rules, and accountability for multiple AI agents — with a full audit trail and real-time overview. Instead of isolated agents running without coordination, they operate under shared governance with clear escalation rules.

An AI context layer is the information layer delivered to an AI agent so it can act correctly within your specific business. It includes company-specific rules, current system data, and historical context. Without a context layer, an AI agent will act based on general knowledge — not your company's precise rules and processes.

An agentic workflow is a fully automated process where one or more AI agents perform a series of actions across systems — from trigger to output — without a manual intermediate step. Example: an incoming email triggers an agent that classifies the inquiry, updates CRM, generates a draft response, and escalates to a human if confidence falls below a threshold.

Human-in-the-loop (HITL) means a human approves, rejects, or adjusts an AI agent's decision before it has consequences. In practice, it is an approval gate in the workflow: the agent stops, flags the task to a named person, and waits for a response. It is the core of responsible AI deployment — and a requirement for high-risk systems under the EU AI Act.

An audit trail is a complete, timestamped log of all actions performed by an AI system: what the agent observed, what decision it made, what action it performed, and what the result was. An audit trail is necessary for GDPR Art. 30 compliance and the EU AI Act, and enables you to investigate and explain any AI decision after the fact.

An approval gate is a defined point in an agentic workflow where the agent stops and requires human confirmation before continuing. Approval gates are used for high-risk or high-consequence actions — such as sending a contract, making a payment, or deleting data. You decide which actions trigger a gate.

AI orchestration is the coordination of multiple AI agents working toward a shared goal. The orchestrator distributes tasks, handles sequence and dependencies, and aggregates results from individual agents into a coherent output. Without orchestration, agents run in isolation — with orchestration, they form an AI workforce capable of handling complex, multi-step processes.

An SLA (Service Level Agreement) in an AI context defines the guaranteed performance parameters for an AI system: response times, uptime, error rate, and escalation time to human handling. In Betterhuman_Corp, SLAs are specified per workflow, so you always know within what parameters your agents operate — and what happens if they fail to meet requirements.

Deployment safeguards are the safety mechanisms set up before an AI system goes into production. This includes error thresholds (when does the agent stop automatically?), fallback handling (what happens during an outage?), and rollback protocols (how is the system reset?). Safeguards are the difference between an AI system tested in a lab and one that is safe in production.