Reference
AI Context Glossary
Plain-English definitions for the key concepts in AI context management, MCP, and the Wire platform.
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AI Agent
An autonomous software program that uses a large language model to plan and execute multi-step tasks, typically by calling tools, reading data, and iterating on its own output.
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AI Hallucination
A confidently-stated but factually incorrect output produced by a language model when it lacks grounded context for a claim.
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AI Second Brain
A personal or team knowledge base built from notes, documents, and references that AI agents can query directly.
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Context as a Service
A model for delivering structured, AI-optimized context to agents and tools on demand.
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Context Compression
The practice of reducing token count in an AI agent's context window while preserving the information needed to complete tasks.
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Context Container
A portable, shareable unit of organized context (documents, data, and structured information) made accessible to AI agents through MCP tools.
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Context Drift
The gradual loss of task-relevant information as an AI agent's context window fills with accumulated history.
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Context Engineering
The practice of deliberately designing, structuring, and managing the information provided to AI models to improve output quality and relevance.
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Context Poisoning
An attack in which malicious or false information is planted into an AI agent's memory, RAG index, or tool outputs so the model treats it as ground truth.
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Context Portability
The ability to use the same context across multiple AI tools and applications without re-uploading or re-configuring.
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Context Rot
The gradual degradation of an AI system's usefulness as the context it relies on becomes stale, incomplete, or outdated.
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Context Window
The maximum amount of text (measured in tokens) that a language model can process in a single inference call.
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Fine-Tuning
A training process that adjusts a pretrained language model's weights on a domain-specific dataset to change its default behavior, style, or task performance.
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MCP (Model Context Protocol)
An open protocol, developed by Anthropic, that standardizes how AI applications provide context and tools to language models.
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MCP Server
A server that implements the Model Context Protocol, exposing data and tools to MCP-compatible AI clients.
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Multi-Agent System
An architecture where multiple AI agents collaborate on a task, each with its own context window, tools, and responsibilities.
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Prompt Caching
A technique that stores computed key-value tensors from a prompt's prefix so they can be reused on subsequent API calls, reducing cost and latency.
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Prompt Engineering
The practice of designing the text of prompts sent to a language model to improve the quality, format, or reliability of its output.
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RAG (Retrieval-Augmented Generation)
A technique that retrieves relevant documents or data at inference time and injects them into the model's context window before generating a response.
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Semantic Search
A search method that finds results based on meaning and intent rather than exact keyword matching.
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Structured Context
Context delivered to AI models as organized, typed records with named fields rather than raw prose or unformatted text.
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Wire
A Context as a Service platform that lets you create composable context containers accessible to AI agents through MCP.
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