The Designer's AI Glossary: Familiar Words, New Meanings

Source: Shannon Harrop, Niki Bamford; Salesforce Design · 2026-06-24

AI has introduced a flood of new vocabulary, from digital labor and multimodal AI to context windows and polyphonic systems. But many of the most important concepts aren’t actually new. They’re familiar words that have taken on new meaning as intelligence becomes embedded in the products we design.

For designers, understanding these shifts matters. AI isn’t just changing interfaces. It’s changing how systems behave, how people build trust, and how work gets done.

Here’s a guide to the terms shaping the future of design. We’ve grouped them around the questions designers increasingly need to answer: How should AI behave? How does it understand? How does it earn trust? And how do we build with it?

Agentic: The shift from software that executes tasks to systems that reason, act, and pursue goals on behalf of users.

Traditional software waits for instructions. Agentic systems can make decisions, coordinate actions, and pursue outcomes with less direct supervision. For example, an AI assistant might proactively schedule a meeting, gather relevant documents, and prepare a summary before the user ever asks. The design challenge is no longer just helping people complete tasks. It’s defining how much autonomy the system should have and where humans remain in control.

Agent behavior design: The practice of designing how AI communicates, asks questions, recovers from mistakes, escalates issues, and collaborates with people and other agents.As AI becomes more capable, behavior becomes a design material. Designers are responsible for defining how an agent responds when it’s uncertain, when it should ask for clarification, when it should escalate to a human, and how it recovers when something goes wrong. A customer service agent, for example, may answer simple questions independently but hand off more sensitive issues to a human representative. Those decisions shape trust just as much as the interface itself.

Multi-agent orchestration: The coordination of humans, agents, tools, and data across complex experiences.

Orchestration once focused primarily on connecting workflows and systems. Today, it often involves coordinating multiple specialized agents working together toward a common goal. A sales agent might gather customer information, a scheduling agent might find time on a calendar, and a compliance agent might review the final recommendation before it is sent. Good orchestration makes those handoffs feel seamless, allowing users to experience one coherent workflow instead of a collection of disconnected systems.

Context: The information that shapes an AI system’s understanding of a situation.

In traditional software, context was often something users had to provide through navigation, filters, or form inputs. AI systems infer context from conversations, behavior, history, location, and surrounding data. A sales rep asking, “What’s the status of this account?” expects AI to understand which account they’re viewing, what opportunities are open, and what conversations have already taken place. Context now plays a central role in determining the quality, relevance, and usefulness of AI outputs.

Prompt: No longer just an instruction, prompts have become a design surface that influences how AI behaves, responds, and collaborates.

As AI systems become more capable, prompts influence behavior as much as output. A prompt can determine how an agent communicates, what information it prioritizes, when it asks follow-up questions, and how it responds when it encounters uncertainty. Designers now craft prompts the way they once crafted flows, interfaces, and UX copy.

Memory: The persistent context that allows AI systems to learn from previous interactions and deliver more personalized, continuous experiences.

Traditional software often starts from a blank slate. AI systems don’t have to. A customer support agent, for example, might remember previous issues, preferences, and unresolved requests, allowing conversations to feel continuous rather than repetitive. Memory transforms interactions into relationships that evolve over time.

Ontology: The system of relationships that helps AI understand how concepts, objects, and data connect.

While memory helps AI remember information, ontology helps it understand information. In a CRM, an ontology might help an AI understand that an account belongs to a customer, a customer has contacts, and those contacts are connected to opportunities and support cases. Those relationships help AI reason across complex systems instead of treating data as isolated facts.

Headless: Traditionally a technical architecture pattern, headless now refers to decoupling intelligence from the interface so AI can operate consistently across channels and touchpoints.

In a headless world, intelligence no longer lives inside a single application. A user might access the same customer data through Slack, Claude, ChatGPT, Microsoft Teams, voice, or a custom workflow. The interface changes, but the underlying system remains the same. In many cases, the conversation itself becomes the interface.

Guardrails: The constraints that define the boundaries of AI behavior.

Guardrails help AI systems stay aligned with user expectations, business requirements, and safety standards. They determine what an agent can do, what information it can access, and when it should escalate to a human. For example, an AI sales assistant might be able to draft customer emails but require approval before sending them. As AI becomes more autonomous, designing guardrails becomes a core design responsibility.

Explainability: The ability of an AI system to make its reasoning understandable to people.

As AI systems take on more responsibility, users need to understand not just what happened, but why. Explainability can take many forms, from citations and confidence indicators to visible reasoning steps and source attribution.