Glossary
The language of shared context
In short
Shared context is the current, permission-scoped record of what a company has decided, committed to and is worried about, kept in one place that both people and AI tools read from. These pages define the terms around it in plain language, one answer per term.
AI context layer
An AI context layer is a shared, governed record of an organisation's decisions, commitments and knowledge that any AI tool or agent can read. It sits between a company's work and its AI tools, so every model answers from the same current business context instead of a blank slate or a hand-written prompt.
Company brain
A company brain is an AI system that answers the way the company itself would, drawing on its decisions, clients, commitments and reasons. It combines three parts: maintained company context, a foundation model, and the company's own agents and skills. The context is the part that changes daily and must be kept current.
Context decay
Context decay is the process by which recorded organisational knowledge becomes outdated while still looking current. Decisions are reversed, owners change and plans move on, but the documents, wikis and AI memories describing them stay as they were. Stale context then misleads people and AI tools more than having no record at all.
Coordination tax
The coordination tax is the time and cost a team spends keeping people aligned rather than doing the work itself: status meetings, re-explaining context to colleagues and AI tools, and searching for past decisions. In Parallel's Coordination Tax Index 2026 measured it at 16.5 hours a week per manager.
Execution drift
Execution drift is the gradual divergence between what an organisation decided to do and what is actually being done. It builds through small, unrecorded changes: a scope adjusted in one meeting, a deadline quietly moved, a dependency nobody passed on. It usually goes unnoticed until a status review or a missed milestone.
Living plan
A living plan is a plan that is updated continuously from the decisions and changes that affect it, rather than rewritten at fixed review points. Each change is traceable to where it was agreed, so the plan reflects the current state of the work instead of the day someone last edited it.
MCP server
An MCP server is a program that exposes data and actions to AI applications using the Model Context Protocol. It describes what it offers as tools, resources and prompts, and any MCP client, such as an AI assistant or coding tool, can connect to it and use those capabilities without a custom integration.
Meeting intelligence
Meeting intelligence is software that records, transcribes and analyses meetings to turn conversation into usable information: summaries, action items, decisions and risks. Basic tools stop at notes for each call; more developed ones connect meetings over time and make what was agreed available to teams and their other systems.
Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open standard for connecting AI applications to external data and tools. Introduced by Anthropic in November 2024, it defines how an MCP client inside an AI application talks to MCP servers that expose tools, resources and prompts, so one integration works across many AI products.
Organisational memory
Organisational memory is the knowledge an organisation retains over time and can draw on when making decisions: past choices and their reasons, commitments, lessons learned and how work is done. It lives in people, documents, systems and routines, and is lost when those are not connected or when people leave.
Permission-scoped context
Permission-scoped context is organisational context served to people and AI agents only within the access rights of the person asking. An assistant or agent reading company knowledge sees what its user is allowed to see and nothing more, so sharing context with AI does not widen who can reach confidential information.
Shared context
Shared context is the common, current understanding a team holds about its work: what was decided, who committed to what, which risks are open and why. When it is written down in one governed place, people and AI tools can work from the same facts instead of each reconstructing them separately.
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