AI Vocabulary

Rules for who can see or do what, often based on roles, attributes, or relationships.

The data persistence layer enabling an agent to retain conversational history, intermediate execution steps, and learned user preferences across tasks.

Standardized protocols and payloads that enable independent AI agents to negotiate, delegate subtasks, and exchange structured facts without human mediation.

AI architectures capable of autonomous goal-driven planning, tool use, iterative reflection, and state mutation, distinguishing them from passive response generators.

A software system combining an LLM reasoning engine with dynamic tool execution, memory, and environment sensors to achieve specific enterprise goals.

An interactive software companion that advises human users, provides auto-completions, and drafts answers within existing productivity applications.

The corporate policies, technical boundaries, audit rails, and compliance controls that ensure AI systems operate ethically, legally, and within risk tolerances.

A mandatory pause in agent execution requiring explicit human validation or higher-level cryptographic authorization before irreversible actions execute.

An immutable chronological log tracking every input, model reasoning step, tool call, approver identity, and database modification.

A conversational program designed to simulate human dialogue, primarily answering user questions via text or voice without back-end task autonomy.

A minimum statistical score or probability bound required for an AI model prediction to be accepted automatically without review.

A standardized software bridge that extracts, synchronizes, or loads data between external systems of record and the AI platform.

The maximum total volume of tokens (input text + output generation) that an LLM can evaluate during a single inference call.

The operational expenditure (inference tokens + compute + human review time) required to bring a multi-step task to certified completion.

A reliable algorithmic software pathway that supersedes probabilistic AI inference when model uncertainty or compliance criteria are breached.

The process of converting unstructured documents (PDFs, invoices, receipts, handwritten slips) into machine-readable structured payloads.

The gradual deterioration in model accuracy or agent relevance caused by shifting data distributions or changing business environments over time.

High-dimensional numerical vectors that encode the semantic meaning and contextual relationships of text, images, or audio for fast similarity comparison.

Rigorous benchmarking frameworks and automated test suites designed to evaluate AI models and agent workflows on accuracy, safety, and latency.

The process of updating internal neural network weights on a smaller, domain-specific dataset to adapt a foundation model for specialized tasks.

Deep learning algorithms capable of creating novel text, code, images, audio, or structured data based on learned patterns from massive corpora.

Anchoring model generations in verifiable, external reference sources (e.g. system of record tables, PDFs, live API responses) rather than parametric memory.

Programmable validation rules, regex filters, and policy bounds wrapped around model inputs and outputs to enforce safety, security, and brand compliance.

A phenomenon where a generative model outputs assertions that appear plausible and confident, but are factually fabricated or unsupported by source data.

A workflow architecture where autonomous agents perform extraction, drafting, and reconciliation, while qualified humans sign off on high-impact actions.

A structured database model that organizes information as real-world entities (nodes) connected by semantic relationships (edges).

Transformer-based neural networks trained on massive corpora, proficient at natural language understanding, synthesis, and logical reasoning.

The time elapsed between a client dispatching a request and receiving a completed response or executed action.

A core security tenet that grants software modules and users only the minimal permissions necessary to fulfill their assigned role.

The branch of computer science where statistical algorithms learn predictive patterns from empirical data rather than manual rule coding.

An open specification developed to standardize how AI agents securely discover resources, connect to context providers, and call tools.

An architecture where specialized software agents collaborate, pass messages, check each other's outputs, and coordinate complex tasks.

The core primitives of semantic modeling: Objects denote real-world entities; Properties define attributes; Links define cardinal relationships.

The capability to measure the internal states of AI agents by analyzing telemetry data, token traces, latency logs, and intermediate reasoning steps.

A comprehensive, dynamic business model defining entities, relationships, rules, and executable operations across the entire enterprise.

The management layer controlling task scheduling, state handoffs, error recovery, and collaboration dynamics across multiple AI agents.

Automated detection and obfuscation of Personally Identifiable Information (SSNs, names, cards) before prompts reach foundation models.

The text input, system instructions, and few-shot examples provided to a large language model to steer its reasoning and response format.

A security vulnerability where malicious or untrusted input tricks an AI model into ignoring its developer instructions or safety parameters.

An architecture that supplements language model queries with live factual chunks retrieved dynamically from external enterprise data stores.

A business abstraction layer that standardizes metrics, KPIs, and joins across data warehouses so BI tools consume unified definitions.

A technique guaranteeing that AI models return data conforming strictly to predefined schemas (e.g. JSON Schema or Pydantic models) without extraneous text.

The basic atomic unit of text (typically ~4 characters or 0.75 words) that language models process and generate during inference.

The mechanism allowing an LLM to select external APIs, format typed parameters, and process return values to trigger actions in external systems.

A database lookup method indexing items by semantic similarity in high-dimensional vector spaces, rather than exact keyword matches.

Workflows follow static deterministic branches predetermined by human code; Agents dynamically choose their execution paths and tools based on runtime context.