Shared work surface
Workspace gives teams and agents a shared environment for drafting, reviewing, and acting on documents, spreadsheets, decks, kanbans, and file viewers.
Context
RivendicaContext is an enterprise AI agents platform for building, deploying, and improving agents on customer infrastructure. It combines workspace, runtime, context, and evaluation tools with permissioned connectors and IdP-based access control.
ContextOverview
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.
Core capabilities
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.
Practical use cases
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.
Pros and Cons
Pros
- Combines workspace, runtime, context layer, and evals in one platform rather than separate point tools.
- Supports enterprise controls such as IdP-based identity, per-action authorization, audit logs, and customer-managed deployment options.
- Includes more than 800 permissioned connectors for working across internal systems and data sources.
- Allows any model or agent framework, including Claude, GPT, Gemini, Kimi, and open weights.
- Stores procedures and corrections as reusable context so future runs can build on prior work.
Cons
- The public pricing page is unavailable, so buyers cannot confirm plan structure from the site.
- The source material is mostly product positioning and screenshots, with limited detail on limits, implementation steps, or commercial terms.
FAQ
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.
Quick Facts
- Category
- AI agents platform
- Primary users
- Enterprise teams building and running AI agents
- Deployment
- Hosted, VPC, on-prem, and air-gapped
- Integrations
- 800+ permissioned connectors
- Source domain
- context.ai
- Pricing
- Not published on the pricing URL