Enterprise AI & Agent Glossary: Key Terms and Definitions

The working vocabulary of enterprise AI: 155 terms CEOs, CISOs, CTOs and heads of AI use when choosing, securing and paying for AI search, copilots and agents. Each one comes with a plain definition and why it matters for today's enterprise AI strategy.

Last updated October 30, 2026

A

Access control list (ACL)

Also called

Related

Permission inheritance

Fine-grained access control

A list attached to a file, record or resource that says which users and groups may access it.

Why it matters

ACLs are where most real permissions live, so AI tools have to respect them item by item. Atolio checks them on every query.

Agent authorization

Also calledauthorization layer

Related

Policy enforcement

Deciding, at the moment an agent makes a request, whether that specific action on that specific data is allowed.

Why it matters

Authentication says who the agent is. Authorization decides what it may do.

Agent builder

Also calledlow-code or no-code agent builder

Related

A tool for creating agents by describing the task, configuring steps or connecting tools, often without writing code.

Why it matters

It puts agent-building in the hands of business teams, which speeds adoption and adds to agent sprawl.

Agent control plane

Also calledagentic control plane, AI control tower

Related

Control plane vs. data plane

A central layer for managing agents across a company: registering them, setting policy, watching what they do and shutting them down. Forrester tracks it as its own market.

Why it matters

Most control planes manage agents without holding the data those agents use, so context and control end up in different places. Atolio keeps both on one governed path and works with the control planes companies already run.

Agent development lifecycle (ADLC)

Also calledagent SDLC

Related

Agent lifecycle management

The SDLC adapted for agents. It adds steps such as scoping data access, evaluating behavior, registering the agent and monitoring it in production.

Why it matters

Agents change behavior when their data or model changes, so they need checks that ordinary software doesn't.

Agent gateway

Also calledAI agent gateway, action gateway

Related

Agent control plane

A control point that agents' requests and actions pass through, where identity, policy, logging and shut-off are enforced.

Why it matters

Without one, every agent has its own route into company systems, and nobody sees them all. In Atolio, every agent call runs through one governed path that also delivers its context, inside the client's perimeter.

Agent identity

Also called

Related

Non-human identity (NHI)

Delegated access

A distinct identity issued to an AI agent, so its access, actions and owner can be tracked on their own.

Why it matters

Without one, agent activity is logged as the person or service it borrowed access from. In Atolio, every agent acts either as the person it works for or under its own identity, and each request is logged to that identity.

Agent lifecycle management

Also called

Related

Tracking each agent from creation to retirement: who owns it, what it can reach, how it's performing, and when to update or shut it down.

Why it matters

Agents that nobody owns keep running, and keep their access.

Agent memory

Also calledmemory layer

Related

What an agent keeps between steps or sessions: earlier messages, decisions and facts about the user or task.

Why it matters

Memory makes agents more useful over time, and it also needs the same access controls as any other data.

Agent observability

Also called

Related

Token cost governance

Monitoring agents in production: their requests, tool calls, latency, errors, cost and output quality.

Why it matters

Agents often fail without an error message, so the only way to know what they did is to watch it.

Agent orchestration

Also called

Related

Multi-agent system

Agentic workflow

Coordinating the order, handoffs and state of one or more agents working on a task.

Why it matters

Orchestration decides what agents do next, but agents still need trusted context to work from. Atolio supplies that context, whichever orchestrator runs the agents.

Agent platform

Also calledagent management platform, enterprise AI platform

Related

Agent control plane

A vendor suite for building, running and governing agents, usually tied to that vendor's cloud or apps. Examples include Microsoft Agent 365, Google Gemini Enterprise and Salesforce Agentforce.

Why it matters

Each governs agents well inside its own apps and cloud. Most companies run agents across several platforms, plus their own. Atolio governs agents built anywhere, from inside the client's own perimeter.

Agent registry

Also calledagent inventory, agent directory

Related

A list of every agent a company runs, with its owner, purpose, identity and access.

Why it matters

You can't govern agents you can't see, and a registry stops teams from building the same agent twice. Atolio registers every agent with its owner, scope and activity.

Agent runtime

Also called

Related

The environment where agents run: it hosts them, manages their sessions and memory, and executes their tool calls.

Why it matters

Cloud providers sell runtimes, such as Amazon Bedrock AgentCore. Wherever agents run, they still need permitted company context, which Atolio supplies to agents in any runtime.

Agent skills

Also called

Related

Packaged instructions, scripts and resources an agent loads when a task calls for them, so it can do specialist work consistently.

Why it matters

Skills are a fast-growing way to give agents repeatable know-how without retraining the model.

Agent sprawl

Also called

Related

Agent lifecycle management

The fast, unmanaged growth of agents across teams and tools, with unclear owners, overlapping jobs and unchecked access.

Why it matters

As agents multiply, few companies can say which agents exist, who owns them and what each can reach. Atolio gives every agent one path in, so the inventory stays current.

Agent washing

Also called

Related

Marketing existing chatbots or automation as "agents" without real agent capabilities.

Why it matters

Gartner, which coined the term, estimates that only a small share of agentic AI vendors offer real agents. One quick check is to ask what the agent can do on its own, without a person prompting each step.

Agent2Agent (A2A) protocol

Also called

Related

Model Context Protocol (MCP)

Multi-agent system

An open protocol that lets AI agents from different vendors find each other, share tasks and exchange results.

Why it matters

It's the main standard for agent-to-agent work, alongside MCP for agent-to-tool.

Agentic AI

Also called

Related

Autonomy levels

AI systems that act toward goals on their own, rather than only responding to a single prompt.

Why it matters

It's the market's umbrella term for the shift from AI that answers to AI that acts.

Agentic RAG

Also calledagentic search

Related

Retrieval-augmented generation (RAG)

Search and retrieval run by an AI agent: it plans a series of queries, reads the results, and refines its approach until it can answer.

Why it matters

It handles multi-part questions better than a single search, but can use far more tokens. Atolio's search agent runs these steps over one index, so the same answer takes fewer passes.

Agentic workflow

Also called

Related

Agent orchestration

Human in the loop (HITL)

A business process where one or more agents carry out some or all of the steps, often with people approving key points.

Why it matters

It's how most companies first put agents into production: inside a defined process, not open-ended.

AI agent

Also called

Related

Software that uses an AI model to pursue a goal in steps: it plans, calls tools, reads results and acts, with limited human input.

Why it matters

Agents can do real work across systems, which also means they can reach real data and take real actions.

AI assistant

Also calledchatbot

Related

A conversational AI tool people ask questions of, such as Claude, ChatGPT or Gemini.

Why it matters

It's where most employees meet AI first, and where ungoverned use usually starts.

AI evaluation

Also calledevals, LLM-as-judge

Related

Agent observability

Testing AI output against a set of questions and expected answers, scored by people, rules or another model acting as a judge.

Why it matters

Evals show whether a change to a model, prompt or data source made answers better or worse before users find out.

AI gateway

Also calledLLM gateway, model gateway

Related

Token cost governance

A proxy between applications and AI models that handles routing, rate limits, caching, cost tracking and policy for model calls.

Why it matters

It controls model traffic but doesn't hold company data, so agents still search each system live for context. Atolio holds the data and the control point together: a permission-aware index with policy on the same path.

AI governance

Also called

Related

AI governance platform

NIST AI Risk Management Framework

The policies, roles and processes that decide how a company builds, buys and uses AI responsibly.

Why it matters

It's now a board-level topic, and frameworks like the EU AI Act make parts of it mandatory.

AI governance platform

Also called

Related

Policy enforcement

Software for setting, applying and proving AI policy across a company: inventories, risk reviews, approvals and compliance records.

Why it matters

Gartner lists AI governance platforms among its top data and analytics trends for 2026. Policy still has to be enforced on live requests; in Atolio, that happens on every agent call.

AI maturity model

Also calledAI journey

Related

Enterprise search

A staged view of how companies adopt AI, typically from search, to grounding the AI tools they already have, to building agents, to running many agents predictably. See Atolio's four stages of AI maturity.

Why it matters

It helps teams see where they are and what each next step needs. Atolio meets companies at any stage, on one foundation, so nothing gets rebuilt along the way.

AI security platform

Also calledAI security posture management (AI-SPM)

Related

Prompt injection

Data security posture management (DSPM)

Tools that find AI models, apps and agents in use, assess their risks, and protect them against AI-specific attacks.

Why it matters

Gartner lists AI security platforms among its top strategic technology trends for 2026. Security teams are adding them alongside existing cloud and data security tools.

AI-ready data

Also called

Related

Permission inheritance

Data classification

Data that AI can use safely and usefully: findable, current, with clear permissions and allowed for the model and region in use.

Why it matters

Readiness depends as much on access rules as on data quality. Atolio inherits permissions exactly as they exist and shows what each person can reach, so teams can start now and find oversharing along the way, instead of undertaking a massive clean-up effort first.

Air-gapped

Also called

Related

On-premises (on-prem)

Government cloud

A system with no connection to the internet or outside networks at all.

Why it matters

It's the strictest form of isolation, often required for classified or highly regulated work. Atolio can run fully air-gapped, on standard infrastructure, with no outside access at all.

Application programming interface (API)

Also called

Related

Model Context Protocol (MCP)

A defined way for one piece of software to request data or actions from another.

Why it matters

APIs and MCP are how AI tools reach other systems, and their limits shape what's possible.

Audit log

Also calledaudit trail

Related

Agent observability

A time-stamped record of what each person or agent asked for, accessed and did.

Why it matters

It's how teams investigate incidents and prove compliance. In Atolio, the audit trail builds itself as agents run, and logs go to destinations the client controls.

Autonomy levels

Also called

Related

Human in the loop (HITL)

A scale for how much an agent may do without a person: from suggesting, to acting with approval, to acting on its own.

Why it matters

It gives teams a shared way to decide which agents need oversight and how much.

C

Caching

Also calledsemantic cache

Related

Storing answers to repeated or similar questions, so they're served again without another model call.

Why it matters

Cached answers cost almost nothing and come back faster. Repeat questions are common at work, so Atolio caches them and they don't hit the model again, driving further cost efficiencies.

Citations

Also calledsource attribution

Related

Links in an AI answer that point to the exact document, message or record each fact came from.

Why it matters

Every company has contradictory data, so people need to see where an answer came from before they act on it. Every Atolio answer cites its sources.

Coding agent

Also called

Related

Software development lifecycle (SDLC)

An agent that writes, edits, tests and reviews software code.

Why it matters

Coding agents are among the most widely used agents today, and they often hold broad access to code and systems.

Collaboration graph

Also called

Related

Enterprise search

Knowledge graph

A map of who works with whom, and on what, built from direct interactions such as emails, direct messages, @-mentions, shared files and comments, and from shared groups, channels and threads. Ties strengthen with recent, frequent contact, fade over time, and shift when someone changes teams.

Why it matters

Two people asking the same question often need different answers. Atolio combines its collaboration graph with the org chart and a document graph of how content links together, so results closest to the person asking rank first, within what they're allowed to see.

Computer-use agent

Also calledbrowser agent

Related

Delegated access

Excessive agency

An agent that operates software the way a person does: reading the screen, clicking, typing and moving between apps.

Why it matters

It can reach anything a person can, which makes its access hard to scope and audit.

Connector

Also calledintegration

Related

Model Context Protocol (MCP)

Software that pulls content, and who can see it, from a business system such as SharePoint, Slack or Jira, and keeps it in sync.

Why it matters

Connector quality decides what AI can see, how fresh it is, and whether permissions come along. Atolio's connectors are permission-aware, and Atolio builds custom connectors for the systems each client needs.

Consumption pricing

Also calledusage-based pricing

Related

Token cost governance

Paying for what you use, such as tokens, credits or agent runs, instead of a fixed price per user. Per-seat pricing is the flat-fee model it's replacing.

Why it matters

Many vendors are moving from seats and credits to consumption, often at renewal. Seeing token spend clearly before that shift lands makes it far easier to plan for.

Context engineering

Also called

Related

The practice of designing what information, instructions and tools an AI model receives, and in what form, so it can do a task well.

Why it matters

Gartner frames it as the successor to prompt engineering. Doing it agent by agent is hard to scale. Atolio gives every agent the same governed context to start from.

Context layer

Also calledcontext engine

Related

Knowledge layer

Context platform

Knowledge scope

The information an AI tool or agent needs for one question or task, at the moment it's asked: the relevant sources, the relevant people, and only what that person or agent is permitted to see. It's built by narrowing a knowledge layer to what's needed and allowed.

Why it matters

Anyone can rent the same AI model; what sets a company apart is its own knowledge. Atolio is the sovereign context layer for enterprise AI: it hands every person, AI tool and agent fewer, better inputs, using up to 80% fewer tokens.

Context platform

Also calledAI context platform

Related

Knowledge layer

Context engineering

Software that constructs, governs and delivers reusable context to AI agents and applications at runtime, so each agent doesn't rebuild it from scratch.

Why it matters

Gartner names AI context platforms one of its top strategic technology trends for 2027, because agents need shared, governed context to act reliably. Atolio builds, governs and delivers permission-aware context from a company's own systems to every AI tool and agent, at the moment each one asks.

Context pollution

Also calledcontext rot

Related

When a model's context fills with irrelevant, duplicate or conflicting material, so answer quality drops and token use rises.

Why it matters

It's a common reason agents give vague or wrong answers even when the right data existed. Atolio filters out what isn't relevant before anything reaches the model, improving cost efficiency, relevance, and usefulness of results.

Context window

Also called

Related

Context pollution

The maximum amount of text, measured in tokens, that a model can consider at once.

Why it matters

Filling a large window costs more and can lower answer quality. Atolio hands the model only the context a task needs, reducing risk surface area and token usage while improving response speed and quality.

Control plane vs. data plane

Also called

Related

Agent control plane

The control plane decides and enforces the rules. The data plane is the path the actual data travels.

Why it matters

A tool that controls agents but isn't in the data path can't see or shape the data agents receive. Atolio sits in the data path, so policy applies to the data itself.

Copilot

Also called

Related

An AI assistant built into the tools people already use, helping them write, search, summarize or code. The term covers the category, not only Microsoft's product.

Why it matters

Most companies already pay for at least one. Atolio grounds Copilot, Claude, ChatGPT and Gemini in every system a company runs, from inside its own perimeter.

Cost attribution

Also calledchargeback, showback

Related

Token cost governance

Assigning AI spend to the people, teams, agents or projects that caused it.

Why it matters

You can't manage or justify spend you can't trace to its source. Atolio reports which connected tools get used, a useful input when reviewing wider tool spend.

Cost per task

Also calledcost per query

Related

Total cost of ownership (TCO)

The average cost to complete one question or job with AI, including every model call and retry.

Why it matters

It's the number that shows whether AI is cheaper than doing the work another way.

Customer-managed keys

Also calledbring your own key (BYOK)

Related

Encryption keys the client creates and controls, rather than the vendor.

Why it matters

Whoever holds the keys controls the data. Atolio runs on the client's own keys and IAM.

D

Data catalog

Also calledAI catalog

Related

An inventory of a company's data assets, with descriptions, owners and lineage.

Why it matters

A catalog tells AI what data exists and what it means. Answering questions also takes the content itself: documents, messages and tickets, with their permissions.

Data classification

Also calledsensitivity labels

Related

Restricted data

Data security posture management (DSPM)

Tagging data by how sensitive it is, such as public, internal, confidential or restricted, often with labels that travel with the file.

Why it matters

Labels let policies treat sensitive data differently, including keeping it away from agents.

Data egress

Also called

Related

Data leaving an environment, such as a company's network or cloud account.

Why it matters

Every place data leaves to is another place it can leak, and cloud providers charge for it too. With Atolio, content stays in the client's network, and only scoped context goes to the model the client chooses.

Data fabric

Also calledknowledge fabric

Related

Knowledge layer

Federated search

An architecture that connects data across many systems through shared metadata and integration, without moving it all into one store.

Why it matters

A data fabric integrates data for analytics and operations. A knowledge layer makes company content searchable for AI, with each item's permissions attached.

Data freshness

Also calledsync, near real time

Related

How current indexed content is compared with its source system.

Why it matters

Agents acting on stale data make stale decisions. Atolio's connectors sync continuously, keeping content near real time, which is current enough for nearly all agent work. For values that must be exact right now, an agent should read the source directly.

Data leakage

Also calleddata loss prevention (DLP), data exfiltration

Related

Runtime inspection

Sensitive information reaching people, systems or vendors it shouldn't. Data loss prevention is the set of tools that stop it.

Why it matters

AI tools can leak data through answers, logs or the model vendor itself.

Data processing agreement (DPA)

Also called

Related

Data sovereignty

A contract that sets how a vendor processes personal data on a client's behalf, as GDPR requires.

Why it matters

It defines who is the data controller and who is the processor, and what each must do.

Data residency

Also called

Related

Data sovereignty

Where data is physically stored and processed, such as a specific cloud region or country.

Why it matters

Many regulations and customer contracts require data to stay in a given region. Atolio runs in the region each client chooses, with separate deployments where strict separation is needed.

Data security posture management (DSPM)

Also called

Related

Data classification

Tools that find sensitive data across a company's systems, show who can access it, and flag risky exposure.

Why it matters

Finding exposure is the first step; the fix happens in each source system. Atolio inherits the corrected permissions automatically.

Data silos

Also calledfragmented knowledge

Related

Knowledge layer

Enterprise search

Information spread across many tools that don't share data or search.

Why it matters

Silos are why people and AI tools can't find what the company already knows. Atolio connects them in one permission-aware index inside the company's own environment, and its collaboration graph surfaces what's relevant to each person, so no one has to know which system holds the answer.

Data sovereignty

Also called

Related

The principle that data is subject to the laws, and the control, of the country or organization where it's held.

Why it matters

It decides which laws apply to your data and who can be forced to hand it over.

Data warehouse and lakehouse

Also called

Related

Structured, semi-structured and unstructured data

Central stores for a company's structured and analytical data, such as Snowflake or Databricks.

Why it matters

Fully structured data usually stays there. Atolio works alongside them: agents reach warehouse data through those systems' own MCP servers.

Delegated access

Also calledon-behalf-of access

Related

Permission-aware retrieval

An agent acting with the access of the person it works for, and no more.

Why it matters

It keeps agents from seeing more than their users could, with no separate permission model to manage. Atolio checks it on every query.

E

Embeddings

Also calledvector embeddings

Related

Vector database

Semantic search

Lists of numbers that represent the meaning of text, so a system can find content that means the same thing even when the words differ.

Why it matters

They let AI search match "time off policy" with "PTO guidelines". Atolio stores embeddings for each item in its index, alongside the item's text and permissions.

Enterprise AI

Also called

Related

Enterprise search

The ways a company puts AI to work: search, where a person asks; copilots, where AI helps a person do the work; and agents, where AI does the work on its own.

Why it matters

All three need the same thing underneath: trusted, permitted access to company knowledge. Atolio powers all three from the same foundation.

Enterprise search

Also calledAI search, cognitive search

Related

Federated search

AI maturity model

Search across a company's internal systems from one place, now usually with AI-generated answers.

Why it matters

It's often a company's first AI project. Atolio started in enterprise search, and the same permission-aware foundation now grounds copilots and agents, so nothing is rebuilt between stages.

EU AI Act

Also called

Related

NIST AI Risk Management Framework

The European Union's law regulating AI by risk level, with duties for providers and users of AI systems, phased in from 2024.

Why it matters

It applies to any company offering or using AI in the EU, wherever it's based.

Excessive agency

Also called

Related

Least privilege

Autonomy levels

OWASP Top 10 for LLM and agentic apps

Giving an AI system more access, tools or autonomy than its task requires. OWASP lists it among the top risks for AI apps.

Why it matters

Many agent incidents trace back to too much permission, with no clever attack involved.

F

Federated MCP

Also calleddirect-to-source MCP

Related

Federated search

Connecting each AI tool or agent straight to each system's own MCP server, so every question fans out to many systems live.

Why it matters

It works for a pilot with a few systems. At scale, every agent pays for every hop and spends tokens on systems with no answer. Atolio's studies show it consumes 80% fewer tokens than federated MCP.

Federated search

Also called

Related

Search that sends each query to every connected system live and merges what comes back, with no central index.

Why it matters

It avoids copying data, but it's only as fast as the slowest system and as good as each system's own search. Atolio indexes instead, so one query ranks across every system at once.

Fine-grained access control

Also calledgranular permissions

Related

Access control list (ACL)

Permission-aware retrieval

Permissions set at the level of individual documents, records, fields or actions, not whole systems.

Why it matters

Whole-system access is too broad for agents, because a single system holds data with very different sensitivity.

Fine-tuning

Also called

Related

Retrieval-augmented generation (RAG)

Foundation model

Further training a model on specific examples to adjust its style or skills.

Why it matters

It changes how a model behaves, but it's a poor way to keep it up to date on company facts. Retrieval does that better.

Foundation model

Also calledfrontier model

Related

Large language model (LLM)

A large, general-purpose AI model that can be adapted to many tasks. Frontier models are the most capable ones available at a given time.

Why it matters

Most enterprise AI is built on a handful of foundation models from a few providers.

G

Generative AI

Also called

Related

Large language model (LLM)

AI that creates new content, such as text, images, code or audio, from a prompt.

Why it matters

It's the broad category that copilots, assistants and agents grew out of.

Government cloud

Also calledGovCloud

Related

Sovereign cloud

Cloud regions built for government workloads, meeting public-sector security and compliance requirements, such as AWS GovCloud.

Why it matters

Public-sector AI often has to run there. Atolio runs in AWS GovCloud.

GraphRAG

Also called

Related

Knowledge graph

Retrieval-augmented generation (RAG)

RAG that also uses a knowledge graph, so the model can follow relationships between people, documents and entities.

Why it matters

Gartner lists it among its top data and analytics trends for 2026. It helps with questions that depend on relationships, such as who owns which account, but it only works as well as the knowledge graph behind it.

Grounding

Also calledgrounded answers

Related

Retrieval-augmented generation (RAG)

Tying an AI answer to specific source material instead of the model's general training.

Why it matters

Grounded answers can be checked, which is what makes them usable at work. Atolio grounds every answer in the client's own systems.

Guardrails

Also called

Related

Policy enforcement

Human in the loop (HITL)

Runtime inspection

Rules and checks that keep AI within set limits, such as what data it can use, what it can say, and what actions it can take.

Why it matters

Guardrails decide whether an agent can be trusted with real work. They hold best when they're enforced on every request, outside the prompt, where an agent can't talk its way past them.

H

Hallucination

Also called

Related

When an AI model states something false or invented as if it were fact.

Why it matters

It's the main reason people don't trust AI answers. Grounding and citations reduce it.

Headless agent

Also called

Related

Unattended agent

An agent with no chat interface, running in the background through APIs, triggers or other systems.

Why it matters

Headless agents are easy to miss in an inventory because nobody sees them working.

Human in the loop (HITL)

Also calledhuman on the loop

Related

Autonomy levels

A person reviews or approves an agent's work before it takes effect. "On the loop" means a person monitors and can step in, without approving each step.

Why it matters

It's the main way companies limit agent risk while trust is still being built.

Hybrid cloud and multi-cloud

Also called

Related

Virtual private cloud (VPC)

Hybrid cloud combines a company's own data centers with public cloud. Multi-cloud uses more than one public cloud provider.

Why it matters

Most large companies run both, so AI infrastructure has to work across them.

Hybrid search

Also called

Related

Semantic search

Enterprise search

Search that combines keyword search and semantic search, then merges the two sets of results.

Why it matters

Keyword search catches exact terms and semantic search catches meaning, so together they miss less. Atolio runs both together, which matters most for company jargon, names and codes.

I

Identity provider (IdP)

Also called

Related

Single sign-on (SSO)

Permission inheritance

The system that manages users and groups and confirms who they are, such as Microsoft Entra ID or Okta.

Why it matters

Group memberships in the IdP decide much of what people, and their agents, can see. Atolio syncs groups from it to keep permissions current.

Index

Also calledindexing, search index

Related

Vector database

Knowledge layer

A searchable copy of content, organized ahead of time so it can be found fast. In enterprise AI, an index can also hold each item's permissions and embeddings.

Why it matters

Ranking across every system in one pass is faster and cheaper than asking each system live, and answers aren't limited by each system's own search. Atolio's index lives in the client's environment, holding each item's text, embeddings and permissions.

Inference

Also calledinference cost

Related

Running a trained model to produce an answer. Inference cost is what each run costs.

Why it matters

Inference is where most ongoing AI spend goes.

Integration platform (iPaaS)

Also calledunified API

Related

Permission inheritance

Software that connects business applications so data and workflows can move between them. A unified API offers one interface to many apps of the same type.

Why it matters

They move data between apps, but usually don't carry each item's permissions, which AI needs to answer safely.

ISO/IEC 42001

Also called

Related

NIST AI Risk Management Framework

An international standard for running an AI management system, which companies can be certified against.

Why it matters

It gives companies and auditors a shared benchmark for responsible AI practices.

K

Kill switch

Also called

Related

Agent lifecycle management

A control that immediately cuts off an agent's access or stops it running.

Why it matters

A misbehaving agent keeps working until someone cuts off every credential it holds. Because every agent comes through Atolio, admins can cut its access from one place.

Knowledge graph

Also called

Related

Collaboration graph

A structured model of entities, such as people, products and customers, and the relationships between them.

Why it matters

It's useful for linked facts. Most company knowledge lives in documents, messages and tickets, which a graph alone doesn't make searchable. Atolio ranks that content with a collaboration graph: who works with whom, and on what.

Knowledge layer

Also called

Related

Permission inheritance

Everything a company has connected for AI use, in one place: the content from its business systems, plus who is allowed to see each item.

Why it matters

Built once, it serves every AI tool and agent, so no tool has to wire itself to every system with its own credentials and permission logic. Atolio builds it inside the client's own environment, pulling from all systems and reflecting all permissions.

Knowledge management (KM)

Also called

Related

Enterprise search

Knowledge layer

The practices and tools a company uses to capture, organize and share what it knows.

Why it matters

It's the discipline enterprise search grew out of, and many KM programs are now being rebuilt around AI.

Knowledge scope

Also calledscoped dataset

Related

Restricted data

Least privilege

A defined set of information an agent is allowed to use, such as two HR SharePoint sites and the HR Slack channel. New content that fits the scope is included automatically.

Why it matters

Scoping an agent to the data its job needs gives better answers, lower token cost and a smaller exposure surface. With Atolio, an agent draws only from its knowledge scope, and within that, only from what the person it acts for is allowed to see.

L

Large language model (LLM)

Also called

Related

Foundation model

An AI model trained on large amounts of text to understand and generate language.

Why it matters

It's the engine inside most AI search, copilots and agents.

Least privilege

Also called

Related

Knowledge scope

Excessive agency

Giving each person, service or agent only the access it needs for its job, and nothing more.

Why it matters

Agents act faster and at larger scale than people, so extra access does more damage. Atolio's knowledge scopes apply least privilege to the data each agent can reach.

Live fetch

Also called

Related

Reading content straight from a source system at the moment of a request, under the asking person's permissions, instead of from an index.

Why it matters

It suits systems with small or fast-changing data. Atolio indexes the systems that hold most of a company's knowledge and fetches live over MCP, under the same permissions.

LLM router

Also calledmodel router, model routing

Related

Token cost governance

Software that decides which AI model handles each request, based on cost, speed, quality or policy.

Why it matters

It lets teams move work to cheaper or better models without rebuilding their apps. Atolio routes agents' model calls through its governed path, with cost and inspection per agent.

M

MCP gateway

Also calledMCP proxy

Related

Control plane vs. data plane

A single point that MCP traffic passes through, used to authenticate, log, filter and control which tools agents can call.

Why it matters

Behind a gateway, agents still search system by system, so token costs climb with every source added. Atolio pairs the gateway with a permission-aware index, so agents get control and context in one place.

MCP registry

Also called

Related

A directory of approved MCP servers and tools that agents and developers can discover and use.

Why it matters

It's how companies stop every team from wiring up unvetted tools on their own.

MCP server

Also calledMCP client, MCP host, MCP endpoint

Related

Model Context Protocol (MCP)

An MCP server exposes a system's data and actions to AI tools. The MCP client inside an AI app calls it, and the host is the app that runs the client.

Why it matters

Many vendors now ship an MCP server, so an AI tool can reach their product without custom code.

Model Context Protocol (MCP)

Also called

Related

An open standard that lets AI tools connect to other systems, such as data sources and apps, through one common interface, one request at a time.

Why it matters

It's quickly become the default way to plug AI tools into business systems. Any AI tool or agent can connect to Atolio through MCP or API.

Model provider

Also calledinference provider

Related

The service that runs an AI model and bills for its use, such as Amazon Bedrock, Azure AI or a model maker's own API.

Why it matters

Where the model runs decides where prompts and data go, and who can see them. Atolio sends AI calls to the client's provider of choice, including models running inside the client's own environment.

Model-agnostic

Also calledbring your own model

Related

Open-weight models

Working with any AI model the client chooses, rather than one built in.

Why it matters

Models change fast. Being model-agnostic lets teams use different models for different jobs and switch without rebuilding or re-indexing. Atolio works with Claude, GPT, Gemini, open models and others.

Multi-agent system

Also called

Related

Agent orchestration

Agent2Agent (A2A) protocol

Several agents working together, each with its own role, handing tasks and results to one another.

Why it matters

Each handoff is another place where data and permissions have to be tracked.

N

NIST AI Risk Management Framework

Also calledNIST AI RMF

Related

A voluntary U.S. framework for identifying, measuring and managing AI risks, organized around four functions: govern, map, measure and manage.

Why it matters

Many companies use it as the backbone of their AI governance program.

Non-human identity (NHI)

Also calledmachine identity, workload identity

Related

Service account

A digital identity used by software, such as a service, script or agent, rather than a person.

Why it matters

Machine identities now outnumber people by more than 80 to 1, according to CyberArk, and agents are adding to them fast.

O

OAuth

Also calledOAuth scopes

Related

Delegated access

Least privilege

An open standard that lets one application act in another on a user's behalf, with "scopes" setting what it may do.

Why it matters

Scopes are coarse. An agent given a broad scope can often reach far more data than its task needs.

On-premises (on-prem)

Also called

Related

Software running on servers in a company's own data centers.

Why it matters

Regulated and public-sector organizations often require it for sensitive data. Atolio runs on-prem as well as in AWS, Azure and Google Cloud.

Open source (OSS)

Also called

Related

Open-weight models

Software whose source code is public, so anyone can inspect, use and change it under its license.

Why it matters

Many teams build AI on open-source frameworks and search engines, then take on the work of running and maintaining them.

Open standards

Also called

Related

Publicly defined specifications, such as MCP and A2A, that any vendor can build on.

Why it matters

They make it easier to switch AI tools and models without rebuilding integrations.

Open-weight models

Also calledopen models

Related

Open source (OSS)

AI models whose trained weights are published, so companies can run them on their own infrastructure.

Why it matters

They let companies run AI fully inside their own environment, including air-gapped.

Oversharing

Also calledover-permissioning, over-provisioned data

Related

Permission-aware retrieval

Data security posture management (DSPM)

Least privilege

When people have access to more information than they should, often through old links, broad groups or default settings.

Why it matters

AI makes oversharing visible: anything a person can technically open, an AI tool working for them can find. Atolio shows every document a person can reach across systems, so teams find oversharing before an agent does.

OWASP Top 10 for LLM and agentic apps

Also called

Related

Prompt injection

Excessive agency

OWASP's ranked lists of the most critical security risks in AI applications and agents, such as prompt injection and excessive agency.

Why it matters

Security teams use them as a checklist when reviewing AI tools.

P

Permission inheritance

Also calledACL inheritance, permission mirroring

Related

Permission-aware retrieval

Role-based access control (RBAC)

Identity provider (IdP)

Carrying each source system's existing access rules into the AI layer, including users, groups and nested groups, and keeping them in sync as they change.

Why it matters

There's no second set of permissions to build or maintain, and access never widens by accident. Atolio inherits permissions from every connected system and syncs groups from the company's identity provider.

Permission-aware retrieval

Also calledpermission-aware search

Related

Permission inheritance

Access control list (ACL)

Retrieval that checks who is asking before anything is returned, so a person or agent only gets content the source systems allow them to see. Permissions are enforced before the data reaches the model, not filtered from its output afterward.

Why it matters

It stops AI from surfacing salaries, deal terms or HR cases to the wrong person. Atolio checks every query against each source's permissions, including group and nested group memberships, so people and agents only get exactly what each system allows.

Policy enforcement

Also calledpolicy-as-code

Related

Agent authorization

Applying governance rules automatically at the moment of each request, often written as code so they're testable and consistent.

Why it matters

Policy that isn't enforced at request time is only a guideline.

Privilege escalation

Also called

Related

Least privilege

Excessive agency

When a user, service or agent gains more access than it was granted, by mistake or through an attack.

Why it matters

Agents chaining tools across systems can create new paths to escalate.

Prompt

Also calledsystem prompt

Related

Context engineering

Prompt injection

The input given to an AI model. A system prompt is the standing instruction, set by the app's builder, that shapes every response.

Why it matters

Prompts steer behavior, which is also why prompt injection works.

Prompt injection

Also called

Related

Runtime inspection

Excessive agency

Hidden or malicious instructions placed in content an AI reads, such as a web page, email or document, to make it do something its user didn't intend.

Why it matters

Any agent that reads outside content can be steered by it.

R

Rate limits

Also calledAPI limits

Related

Federated search

Caps a system sets on how many requests a client can make in a given time.

Why it matters

When many agents call the same system live, they hit these caps and stall. Atolio indexes content ahead of time, so agents search one index instead of calling each system live, and stay clear of each system's limits.

Reasoning model

Also called

Related

An AI model that works through a problem in steps before answering, trading speed and tokens for accuracy.

Why it matters

Reasoning models handle harder tasks but can use many more tokens per answer.

Reranking

Also calledre-ranking

Related

Collaboration graph

A second pass that reorders search results by relevance, often with a more precise model, before they reach a person or an AI model.

Why it matters

The top results are what the model reads, so their order shapes the answer. Atolio ranks with signals like who works with whom, so the most relevant content comes first for each person.

Restricted data

Also calledrestricted knowledge scope

Related

Knowledge scope

Sensitive information that admins mark off-limits to every agent by default, even when the person running the agent can see it. Access is granted per agent, on approval, and audited.

Why it matters

Data like HR investigations shouldn't reach an agent just because its user can open it. Atolio keeps restricted data out of every agent's scope until its owner approves access.

Retrieval

Also called

Related

Retrieval-augmented generation (RAG)

Permission-aware retrieval

Finding and fetching the information an AI model needs to answer a question or do a task, from an index or straight from source systems.

Why it matters

Retrieval decides what the model sees, so it sets the ceiling on answer quality, cost and safety. Atolio's retrieval is permission-aware, ranked across every connected system, for more cost efficient, relevant, and useful results.

Retrieval-augmented generation (RAG)

Also called

Related

Permission-aware retrieval

A method where the system first searches for relevant information, then has the AI model answer from what it found.

Why it matters

It grounds answers in company data the model never saw in training, which cuts hallucinations. The answer is only as good, and as safe, as what retrieval hands the model.

Role-based access control (RBAC)

Also called

Related

Least privilege

Permission inheritance

Granting access by job role, such as "finance analyst", instead of person by person.

Why it matters

AI and agents should follow the roles a company already manages, not a new model built just for AI.

Runtime inspection

Also calledprompt and response inspection

Related

Prompt injection

Checking prompts, responses and tool calls as they happen, to block sensitive data, unsafe output or policy breaches.

Why it matters

It catches problems in live traffic, before they reach a person or a system. Atolio inspects prompts and responses on the path that every agent's data and model calls pass through.

S

SaaS vs. single-tenant

Also calledmulti-tenant

Related

SaaS usually means multi-tenant: many clients share the vendor's infrastructure. Single-tenant gives each client its own isolated instance.

Why it matters

The deployment model decides whose infrastructure your data lives on, and who else shares it.

Sandboxing

Also called

Related

Least privilege

Prompt injection

Running an agent or its code in an isolated environment, so it can't reach systems or data beyond what's allowed.

Why it matters

It limits the damage if an agent misbehaves or is tricked.

Self-hosted

Also calledcustomer-hosted, in-tenant

Related

On-premises (on-prem)

Virtual private cloud (VPC)

Software that runs in the client's own cloud account or data center, operated under the client's own controls, instead of in the vendor's cloud.

Why it matters

Data and AI calls stay inside the company's perimeter, under its own IAM and keys, and the vendor can't see them. Atolio is fully self-hosted, in the client's cloud account or on-prem.

Semantic layer

Also called

Related

Data warehouse and lakehouse

A shared set of business definitions, such as what counts as revenue or an active customer, that sits over a company's structured data.

Why it matters

It helps AI read metrics consistently, but it covers structured data only, not documents, messages or tickets.

Semantic search

Also calledvector search

Related

Search that finds content by meaning rather than exact words, usually by comparing embeddings.

Why it matters

It finds the right document even when people phrase things differently, but it can miss exact terms like product codes, names or ticket numbers.

Service account

Also called

Related

Non-human identity (NHI)

Unattended agent

An identity created for a system or automated process rather than a person.

Why it matters

Service accounts often carry broad, long-lived access that nobody reviews.

Shadow AI

Also calledrogue agents

Related

AI tools and agents that employees use or build without IT's knowledge or approval.

Why it matters

People turn to shadow AI when approved tools can't find what they need, so banning it rarely works. Atolio brings company knowledge into the AI tools people already use, with permissions intact.

Single sign-on (SSO)

Also calledOIDC

Related

Identity provider (IdP)

One login that gives a person access to many applications, usually through standards like OpenID Connect (OIDC).

Why it matters

SSO ties AI tools to the identities a company already manages, so people sign in once instead of to every tool. Atolio uses the company's existing identity provider.

Software development lifecycle (SDLC)

Also called

Related

Agent development lifecycle (ADLC)

The stages software goes through: plan, build, test, release, operate and retire.

Why it matters

Agents are software, and AI is changing every stage, from coding agents to automated review.

Sovereign AI

Also calledprivate AI

Related

Data sovereignty

AI that a company controls end to end: it runs in the company's own environment, under its own keys, with models it chooses, and can't be reached from outside. The term has no single industry definition, so it helps to ask which layers stay under your control: compute, models, data and operations.

Why it matters

Secure describes how well a vendor protects your data on its systems. Sovereign means the data, models and infrastructure stay under your control, which you can check. Atolio runs inside the client's environment, on the client's keys, with any model.

Sovereign cloud

Also called

Related

Data sovereignty

Cloud services built to keep data, operations and staff access within one country's legal jurisdiction.

Why it matters

It's a growing requirement in Europe and the public sector.

Structured, semi-structured and unstructured data

Also called

Related

Data warehouse and lakehouse

Structured data lives in tables, like a data warehouse. Semi-structured data is records with typed fields, like CRM accounts and tickets. Unstructured data is free text, like documents, messages and email.

Why it matters

Most company knowledge is unstructured or semi-structured. For supported connectors, Atolio indexes records like Salesforce accounts and cases with their typed fields and permissions, so searches can filter on them.

System of record

Also calledsource system

Related

The authoritative system for a type of data, such as the CRM for customer accounts.

Why it matters

AI should read from systems of record, not replace them. With Atolio, source systems stay the system of record.

T

Token

Also called

Related

The unit AI models read and write in, roughly three-quarters of a word. Model providers bill by the number of tokens in and out.

Why it matters

Tokens are the meter on every AI bill.

Token cost

Also calledtoken spend, AI spend

Related

Token cost governance

What a company pays model providers for the tokens its AI tools and agents use.

Why it matters

Agents can use many times more tokens than a person chatting. Sending the model fewer, better inputs is the most direct way to bring the bill down: Atolio uses up to 80% fewer tokens than federated MCP.

Token cost governance

Also calledAI cost governance, FinOps for AI, token budgets, spend controls

Related

Cost attribution

Consumption pricing

Policies and tools that set, track and limit token spend by team, user, agent or use case. It applies FinOps practices, such as tagging, budgets, forecasting and showback, to AI.

Why it matters

Without it, the first sign of a problem is the invoice. Atolio attributes spend as it happens, per user and per agent, by model and over time, so teams can plan from real numbers.

Tokenomics

Also calledtoken economics

Related

Consumption pricing

How token use translates into cost and value: what drives consumption, what each task costs, and whether the result is worth it.

Why it matters

IDC and others expect token use to grow sharply as agents spread, so the economics decide which AI projects scale.

Tool calling

Also calledfunction calling, tool use

Related

An AI model's ability to call external functions or APIs, such as searching a database or creating a ticket, as part of answering.

Why it matters

It's what turns a model that writes text into an agent that does work.

Tool poisoning

Also calledMCP poisoning

Related

Prompt injection

Tampering with a tool's description or behavior, often through an MCP server, so an agent that uses it is misled or hijacked.

Why it matters

Agents trust tool descriptions. A poisoned tool can turn a trusted agent against its user.

Total cost of ownership (TCO)

Also called

Related

Consumption pricing

The full cost of a system over its life: licenses, infrastructure, model use, implementation and the people to run it.

Why it matters

AI tools with similar list prices can differ widely once infrastructure and model costs are included. With Atolio, clients pay a software license and pay their cloud and model providers directly, with no markup.

Traceability

Also calledprovenance, lineage

Related

Being able to follow an answer or action back to its sources, the agent that produced it, and the person it acted for.

Why it matters

"The AI did it" isn't an answer for regulators or auditors.

U

Unattended agent

Also calledautonomous agent

Related

Service account

An agent that runs on its own, on a schedule or a trigger, without a person present at the time.

Why it matters

With no person behind each request, it needs its own identity and tightly set access. In Atolio, unattended agents run under a service account or agent identity, with access the admin sets.

V

Vector database

Also called

Related

Semantic search

A database built to store embeddings and find the closest matches quickly.

Why it matters

It's a common starting point for teams building AI search themselves. Connectors, permission sync and ranking across systems still have to be built, then maintained. Atolio is that finished layer, so engineers can focus on the agents themselves.

Vendor lock-in

Also calledmodel lock-in

Related

Open source (OSS)

Depending on one vendor so heavily that switching becomes too costly or disruptive.

Why it matters

AI is moving fast, and today's best model or platform may not be next year's. Atolio runs on the client's own infrastructure and works with any model and AI tool, so the company keeps its options open.

Virtual private cloud (VPC)

Also called

Related

Hybrid cloud and multi-cloud

A private, isolated section of a public cloud, such as AWS, Azure or Google Cloud, that a company controls.

Why it matters

It's the most common place self-hosted software runs.

Z

Zero trust

Also called

Related

Least privilege

Agent authorization

A security model that trusts no user, device or request by default and checks every access attempt.

Why it matters

Agents fit the model only if each of their requests is checked as well.

No terms match your search. Try a shorter word or an acronym, such as MCP or RAG.

See how Atolio powers your enterprise AI strategy