Context

Context란 무엇인가요?

Context is a platform for building, deploying, and improving AI agents in enterprise settings. The site frames it as a unified foundation made up of Workspace, Engine, Unify, and Evals, with the shared goal of putting agents into production on customer infrastructure.

Across the product pages, Context emphasizes controlled execution, institutional context, and evaluation loops. Agents can work with a customer’s identity provider, use permissioned connectors, and ground their outputs in procedures, prior work, and expert corrections that are stored for future runs.

Context(으)로 무엇을 할 수 있나요?

Shared work surface

Workspace gives teams and agents a shared environment for drafting, reviewing, and acting on documents, spreadsheets, decks, kanbans, and file viewers.

Controlled execution runtime

Engine provides identity, tools, compute, and permissions on customer infrastructure, with per-session sandboxes and authorization before each action.

Institutional context layer

Unify turns procedures, prior work, and expert corrections into a .context filesystem and context graph that agents traverse during future runs.

Evaluation and gating

Evals uses rubrics and golden sets to validate runbooks, models, and context changes before they ship, so regressions can be caught automatically.

Permissioned system access

The platform exposes 800+ permissioned connectors and attaches credentials through the gateway instead of placing them inside the sandbox.

Flexible deployment options

The site says the platform supports hosted, VPC, on-prem, and air-gapped deployments, including an on-prem appliance.

사용 사례

“Collaborative production work”

Use Workspace when analysts or operators need to draft and review work products alongside agents, such as account reviews, memos, spreadsheets, and file-based research.

“Controlled agent execution”

Use Engine when you need an agent runtime that respects enterprise identity, scoped tools, and explicit approvals before sensitive actions are taken.

“Institutional knowledge reuse”

Use Unify when you want firm procedures, expert corrections, and prior work to accumulate into a reusable context layer that improves future runs.

“Quality gating before release”

Use Evals when you need rubrics and golden sets to check whether model, runbook, or context changes preserve output quality over time.

“Restricted-environment deployment”

Use the platform in regulated or isolated environments when hosted, VPC, on-prem, or air-gapped deployment options are required.

자주 묻는 질문

What is Context?

Context is positioned as a unified platform for building, deploying, and improving AI agents. The source pages describe Workspace, Engine, Unify, and Evals as the main modules in that platform.

Where can it be deployed?

The site describes a production environment that can run hosted, in your VPC, on-prem, or air-gapped. It is built for teams that need agents to work across internal systems with permissions, audit logs, and controlled tooling.

Is pricing published on the site?

The source does not show self-serve pricing on the pricing page. The pricing URL returns a page-not-found screen, and the site repeatedly uses a book-a-demo or talk-to-us flow.

What models can it use?

Context supports any model or agent framework on the platform, including Claude, GPT, Gemini, Kimi, and open weights, according to the homepage.

How does access control work?

The product pages say agents authenticate through the customer’s IdP and inherit the invoking user’s permissions, with actions authorized before they touch data.