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Palette: Artifact, Chat, Checkpoint, Connection, Connector Gateway, Context Library, Context page, Handoff, Palette agent, Palette Desktop, Palette OS, Plan mode, Reference, Session, Skill, Workspace AI basics: API key, Context, Context window, Evaluation, Generative AI, Grounding, Hallucination, Large language model, Model, Model provider, On-device model, Prompt, Reasoning model, Retrieval-augmented generation, Token Agents: AI agent, Agent harness, Agent memory, Human in the loop, Instruction file, Model Context Protocol, Permissions, Prompt injection, Sandbox, System prompt, Tool use Teams and context: Agent-ready workspace, AI adoption lead, AI-native team, Context engineering, Governance, Markdown, Organizational context, Organizational memory, Prompt engineering, Scope, Shared context, WorkflowA
Agent harness
Category: Agents The software layer that runs an agent. It gives a model its instructions, tools, permissions, and working loop, then manages what happens between each model response. Claude Code, Codex, Gemini CLI, Mistral Vibe, and the Palette agent are different harnesses available in Palette Desktop. Related: AI agent, Model, Tool useAgent memory
Category: Agents Information an agent can retain and use beyond the immediate message. Memory can come from saved conversation history, files, instruction files, or a shared system such as the Context Library. It is different from a model’s context window, which only limits how much information can be considered at one time. Related: Context window, Instruction file, Organizational memoryAgent-ready workspace
Category: Teams and context A folder or shared environment organized so an agent can understand and work in it without a long explanation every time. Clear names, useful README files, written instructions, examples, and current decisions all help. Agent-ready does not mean machine-only. The same structure should make the work easier for people to navigate. Related: Instruction file, Shared context, WorkspaceAI adoption lead
Category: Teams and context The person responsible for helping AI work across a company or team, regardless of their formal job title. They test tools, help colleagues adopt new ways of working, set practical rules, and often discover the company’s context and governance gaps first. Related: AI-native team, Governance, Shared contextAI agent
Category: Agents A system that uses an AI model to work toward a goal over one or more steps. An agent can inspect information, choose tools, take actions, check the result, and continue. A model generates responses, while an agent adds the working loop and the ability to act. Related: Agent harness, Model, Tool useAI-native team
Category: Teams and context A team that treats AI as part of how work gets done, not as an occasional writing assistant. People still set direction and make important decisions, while agents help research, draft, analyze, and execute. Buying AI licenses alone does not make a team AI-native. Related: AI adoption lead, Human in the loop, WorkflowAPI key
Category: AI basics A secret credential that lets software use a service, such as a model provider, on your account. When an agent uses your API key, usage is normally billed by that provider. Treat an API key like a password and never put it in a shared document, prompt, or public file. Related: Model provider, TokenArtifact
Category: Palette A file or output produced by a person or agent that can be kept, shared, or used in later work. In Palette, artifacts include shared Markdown and interactive HTML outputs. The useful distinction is between the conversation where work happened and the artifact worth carrying forward. Related: Handoff, Organizational memory, WorkspaceC
Chat
Category: Palette A conversation with one agent inside a Palette Desktop session. A session can contain several chats working on different parts of the same task, while all of them share the session’s working copy of the folder. Learn more about Chats in Palette Desktop. Related: AI agent, Session, WorkspaceCheckpoint
Category: Palette A saved snapshot of work in a Palette Desktop folder or session. Checkpoints create a history you can inspect and restore, so experimentation does not depend on remembering every change or undoing it by hand. Related: Session, WorkspaceConnection
Category: Palette An organization-level link between Palette and a team tool such as Slack, Linear, GitHub, or Notion. Connections provide source material that can help keep the Context Library current. They are different from personal tools made available to agents through Connector Gateway. Learn more about how the Context Library works. Related: Connector Gateway, Context Library, Model Context ProtocolConnector Gateway
Category: Palette An early Palette capability for connecting a personal tool once and making it available across the agents you use. The connection uses your own account and permissions. Google Calendar is the current pilot, so Connector Gateway should not yet be understood as a broad catalog of personal connectors. Related: Connection, Model Context Protocol, PermissionsContext
Category: AI basics The information available to a model when it produces a response. Context can include your prompt, conversation history, instructions, files, retrieved facts, and tool results. Better context usually improves relevance, but more context is not automatically better if it is stale, noisy, or contradictory. Related: Context engineering, Context window, Shared contextContext engineering
Category: Teams and context The practice of deciding what information an AI should receive, how it should be structured, and how it stays current. Prompt engineering improves the request. Context engineering improves the information environment around the request. Related: Context, Prompt engineering, GroundingContext Library
Category: Palette The Palette OS feature that organizes scoped context pages for an organization, its teams, and its users. Palette uses selected company activity and check-ins during a weekly generation step. People and supported agents can read the pages their permissions allow. Learn more about the Context Library. Related: Connection, Context page, Shared contextContext page
Category: Palette An individual document in the Context Library, such as a company overview, team brief, or person profile. A page belongs to an organization, team, or user scope, which controls where it is available. Context pages are designed to be readable by both people and agents. Related: Context Library, Scope, Shared contextContext window
Category: AI basics The maximum amount of information a model can consider in one request, measured in tokens. The window may contain instructions, chat history, files, and tool results as well as your latest message. A larger window helps with bigger tasks, but does not give a model permanent memory. Related: Agent memory, Context, TokenE
Evaluation (evals)
Category: AI basics A repeatable way to test how well a model, agent, prompt, or workflow performs a defined job. An evaluation might check factual accuracy, whether instructions were followed, or whether an action completed safely. Good evaluations use realistic examples and clear criteria instead of judging a few impressive outputs. Related: Grounding, Hallucination, WorkflowG
Generative AI
Category: AI basics AI systems that create new content such as text, images, audio, video, or code in response to instructions and context. The output is generated from patterns learned during training, which is why it can be useful and fluent without being guaranteed correct. Related: Hallucination, Large language model, ModelGovernance
Category: Teams and context The rules and responsibilities that shape how a company uses AI. This includes approved tools, data access, retention, permissions, human review, and who is accountable when an agent takes an action. Good governance makes safe use easier instead of reducing everything to a ban. Related: Human in the loop, Permissions, ScopeGrounding
Category: AI basics Connecting an AI response to relevant sources instead of relying only on what the model learned during training. Files, company context, search results, and tool data can all provide grounding. Grounding reduces unsupported answers, but a source can still be incomplete or wrong, so important claims may still need review. Related: Context, Hallucination, Retrieval-augmented generationH
Hallucination
Category: AI basics An answer generated by an AI model that sounds plausible but is unsupported or incorrect. A hallucination is not deliberate deception. The model is producing likely text rather than checking truth by default. Grounding, evaluations, and human review reduce the risk. Related: Evaluation, Grounding, Human in the loopHandoff
Category: Palette A package of work and context passed from one person or agent to another so they can continue without starting again. In Palette, a handoff can include a summary, instructions, references, and the intended recipient or team. It can be opened into a new or existing Desktop session. Related: Artifact, Organizational memory, SessionHuman in the loop
Category: Agents A way of working where a person reviews, approves, corrects, or redirects an agent at meaningful points. The human does not need to approve every small step. The goal is to place judgment before consequential actions such as publishing, sending, spending, deleting, or saving changes into shared work. Related: Governance, Permissions, Plan modeI
Instruction file
Category: Agents A file that tells an agent how to work in a folder or project. Files such asAGENTS.md and CLAUDE.md can describe the team, structure, conventions, and checks an agent should follow. Different agent tools read different filenames, so the instructions should remain consistent across the files your team uses.
Learn more about working with AGENTS.md and CLAUDE.md.
Related: Agent-ready workspace, System prompt, Workspace
L
Large language model (LLM)
Category: AI basics A model trained on large amounts of text so it can understand and generate language. Models such as Claude, GPT, Gemini, and Mistral can summarize, draft, classify, and reason over text. An LLM is the engine, not the complete agent that plans work and uses tools. Related: AI agent, Model, Model providerM
Markdown
Category: Teams and context A plain-text format that adds simple structure with headings, lists, links, and other marks. Markdown files use the.md extension and remain readable without special software. That makes them useful for shared context because people and agents can both read and edit them reliably.
Related: Agent-ready workspace, Artifact, Instruction file