3.7 — Vocabulary Bank (Phase 3)
| Term | Definition |
|---|---|
| Agent | AI system with tools, decision-making ability, and multi-step execution capability |
| Tool use / Function calling | Mechanism letting AI take actions beyond generating text — read files, call APIs, run commands |
| MCP (Model Context Protocol) | Standard protocol for connecting AI agents to external tools and data sources |
| MCP Server | Program that exposes tools from an external system to an AI agent |
| Hook | Script that runs automatically when a specific event occurs in the harness |
| RAG (Retrieval-Augmented Generation) | Retrieving relevant information from a knowledge base and injecting it into AI context |
| Embedding | Numeric representation of text used to measure similarity between documents |
| Vector database | Database optimized for storing and searching embeddings |
| Semantic search | Search by meaning rather than exact keyword match |
| Guardrail | A constraint placed on an agent to limit its actions — a safety boundary |
| Human-in-the-loop | Design pattern where an agent pauses for human approval at critical decision points |
| Autonomy | The degree to which an agent can act without human direction |
| Orchestration | Coordinating multiple agents or systems to accomplish a goal |
| Pipeline | A sequence of processing steps where the output of one becomes the input of the next |
| Inference | When an AI model processes input and generates output. Every response = one inference call. Inference costs money. |
| Latency | Time between sending a request and receiving a response — the delay |
| Throughput | How many requests a system can handle in a given time period |
| Token budget | The maximum tokens allocated for a task or session — controls cost |
| Deterministic | Same input always produces the same output. Traditional code is deterministic. AI is not. |
| Non-deterministic | Same input can produce different outputs each time. AI models are non-deterministic. |
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