AI terms, explained plainly.

AI systems that can plan, take multi-step action, and complete tasks with limited human input — not just answer questions. Unlike a chatbot, an agentic system can monitor data, make decisions within set boundaries, and execute across tools and departments on its own.

A hypothetical future AI that matches or exceeds human-level capability across virtually any intellectual task — not yet achieved by any lab. Agentic AI is a present-day, narrower capability (multi-step task execution); AGI is a broader, still-unrealized goal several labs are actively racing toward.

An open standard that lets AI agents connect to external tools and data sources in a consistent way — the "how an agent talks to your software" layer. Now maintained under the Linux Foundation's Agentic AI Foundation.

An open standard that lets AI agents from different vendors and systems talk directly to each other — the "how one agent talks to another agent" layer, complementing MCP's tool-connection role.

An AI system built around one specific company's own data, workflows and definitions — as opposed to a generic, off-the-shelf tool applied unchanged across many businesses. Companies that redesign core processes around custom AI report significantly higher revenue impact and lower costs than those bolting generic tools onto unchanged workflows.

The named ownership, oversight rules, and human-review steps that keep AI systems accountable — who's responsible when something goes wrong, what gets automated versus reviewed, and how decisions are audited. Missing governance is the most common reason agentic AI projects get cancelled.

Continuous, cross-department monitoring that identifies exactly where a business is losing money — a stalled deal, a missed follow-up, a bottleneck between teams — before it compounds into a bigger loss. Core to how HALU™ operates.

Neural Horizons AI's autonomous cross-department agent. It monitors Sales, Marketing, Operations, Finance and Leadership simultaneously, flags revenue leaks and bottlenecks, and generates and routes action items — without waiting to be asked.

The AI operating platform underneath HALU™ — the infrastructure layer for businesses running AI systems at scale, built to be governed and auditable from the start rather than retrofitted later.

An AI tool or agent that operates inside a single department, disconnected from the rest of the business — the majority pattern today. The vast majority of AI agents that stay siloed never reach full production; the ones that connect across departments are far more likely to deliver measurable ROI.

Deliberately training existing employees to work alongside AI systems rather than replacing them outright. A large share of the workforce will need meaningful reskilling within the next two years — treated as a wellness add-on, it fails; treated as core deployment strategy, it compounds productivity gains.

Gartner's term for what happens when AI agents complete tasks directly across multiple systems, bypassing the traditional software interfaces built for human users — putting an estimated $234 billion of enterprise SaaS spending at risk through 2030.