Governed enterprise workflows
Automate repeatable work that spans documents, multiple systems, approvals, and expert judgment.
Independent tool overview
Context at a glance
Context is an enterprise execution platform for building, running, evaluating, and improving AI agents on infrastructure an organization controls.
Visit the official Context site ↗
- Product type
- Enterprise AI agent platform
- Workflow definition
- Plain-English runbooks
- Deployment
- Managed, customer VPC, on-premises, or air-gapped
- Model choice
- Claude, GPT, Gemini, Kimi, and open weights
- Identity
- Okta or Entra ID with per-action authorization
- Pricing
- Custom quote
Overview
What Context is
Context has changed substantially from the AI Office Suite introduced in 2025. The current product is an enterprise agent platform: teams describe repeatable work as plain-English runbooks, connect approved systems, run agents in isolated environments, and score results against organization-specific rubrics.
Its four main layers cover a shared workspace, an execution engine, institutional knowledge, and evaluations. Agents can work with documents, spreadsheets, decks, and connected business systems while inheriting user identity and passing each proposed action through authorization policy.
Context can be managed by the vendor or deployed in a customer's VPC, on-premises, or in an air-gapped environment. Pricing is quote-based, so it is best suited to organizations with a high-value repeatable workflow and the security or governance maturity to operate production agents.
Use cases
Who Context is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Sensitive or regulated data
Deploy the control plane, execution runtime, traces, and credentials inside a customer-controlled VPC, on-premises environment, or air gap.
Quality-measured agents
Use rubrics, golden sets, audit trails, and accepted outputs to test whether an agent meets the organization's definition of good work.
Model-flexible operations
Route different steps to frontier or open-weight models instead of committing every workflow to one model vendor.
Capabilities
Core Context features
Shared agent workspace
People and agents work with documents, spreadsheets, presentations, kanbans, and file viewers in the same environment.
Durable runbooks
Teams define repeatable procedures, tools, approvals, and expected outputs in plain language rather than relying on one-off chats.
Institutional knowledge layer
A structured filesystem stores domain documents and what agents learn from reviewed work so future runs can use organization-specific context.
Evaluations and routing
Rubrics and golden sets can gate workflow changes, while step-level routing selects a model that meets quality requirements at a lower cost.
Identity and authorization
Agents are first-class principals tied to the organization's identity provider, with policy checks before actions and brokered credentials at runtime.
Flexible deployment
Run Context as a managed service, inside an AWS, Azure, or Google Cloud VPC, on premises, or in a disconnected environment.
Multiple work surfaces
Use the web workspace, Microsoft Teams, Slack, desktop tools, or Context Code in the terminal while keeping runs visible to the team.
Process
How the Context workflow works
Step 1
Choose one valuable process
Start with a repeatable workflow that has clear inputs, systems, review points, and a measurable definition of done.
Step 2
Select the deployment boundary
Decide whether managed hosting, a customer VPC, on-premises infrastructure, or air-gapped operation matches the data and compliance requirements.
Step 3
Define identity and tools
Connect the identity provider, approve only the required systems, separate read from write permissions, and establish credential rotation and revocation.
Step 4
Author the runbook and rubric
Capture the steps, approval gates, examples, source requirements, edge cases, and scoring criteria used by human experts.
Step 5
Pilot and audit
Run a representative evaluation set, inspect every action and output, test failure and revocation paths, and expand only after the workflow passes.
Cost
Context pricing and free plan
Context does not publish per-seat or platform prices. Quotes depend on the deployment model, usage, support requirements, and implementation scope; model-inference charges and customer-operated infrastructure can add to the total cost.
Managed deployment
Custom quote
The vendor operates the deployment when a hosted control plane fits the organization's policy.
- Fastest deployment path
- Pricing varies with usage, support, and implementation
- Document model-provider retention and data flows before launch
Customer VPC
Custom quote
Run the platform in the organization's AWS, Azure, or Google Cloud account.
- Customer controls networking, keys, stores, and operational access
- Customer cloud and model-inference costs apply
- Requires Kubernetes and security-operations capability
On-premises or air-gapped
Custom quote
Deployment for organizations that need the entire platform inside a controlled or disconnected perimeter.
- Implementation and support scope are negotiated
- Customer operates the infrastructure
- Confirm update, incident-response, and model-serving processes
Pricing checked . Check current pricing at the source ↗
Assessment
Context strengths and limitations
Where it stands out
- Supports the entire agent platform inside customer-controlled infrastructure, not only private model inference
- Combines runbooks, identity, tools, institutional knowledge, evaluations, and audit in one system
- Model choice reduces dependence on a single AI provider
- Per-action authorization and runtime credential brokering are better suited to production workflows than unrestricted shared tokens
- Built-in office-file surfaces and chat or terminal access cover both business and engineering tasks
- Evaluation rubrics make agent quality measurable against a company's own standards
What to consider
- There is no self-serve public price, making cost comparison and small-team adoption difficult
- VPC, on-premises, and air-gapped deployments require meaningful implementation, infrastructure, security, and support work
- Context's public performance figures are internal vendor benchmarks rather than independent evaluations
- An April 2026 Vercel security bulletin says a compromise of Context.ai was used to take over an employee's Google Workspace account and pivot into Vercel; buyers should include that incident and the resulting remediations in vendor diligence
- The current Trust Center says compliance materials for its present audit cycle are available on request and does not publicly list completed certifications on the overview page
- Broad connectors and agent permissions create material risk unless OAuth grants, credentials, approvals, logs, and revocation are tightly governed
Compare
Context alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Business Operations
Microsoft Copilot
A better fit when the main requirement is an assistant embedded directly across Microsoft 365 with published seat pricing.
Explore Microsoft Copilot →Agents
Zapier Agents
A more accessible option for building cross-app agents and automations without operating an enterprise agent platform.
Explore Zapier Agents →Business Operations
ChatGPT
A general-purpose assistant platform with faster self-serve rollout when most work can stay inside individual chat and agent sessions.
Explore ChatGPT →Questions
Context FAQs
What is Context AI?
Context is an enterprise platform for defining AI work as runbooks, running agents with approved tools and identity, grounding them in company knowledge, and scoring the results with organization-specific evaluations.
Is Context still an AI Office Suite?
The company has substantially repositioned the product. The current Context offering is an enterprise agent execution platform, although it still includes working surfaces for documents, spreadsheets, presentations, and other files.
Where does Context run?
It supports a vendor-managed deployment, a customer's AWS, Azure, or Google Cloud VPC, on-premises infrastructure, and air-gapped environments. The selected architecture determines the data boundary and operating responsibilities.
Which AI models does Context support?
Context describes the platform as model-agnostic and lists Claude, GPT, Gemini, Kimi, and open-weight models. Model availability, retention, and routing depend on the deployment design.
How much does Context cost?
Pricing is quote-based and depends on deployment, usage, support, and implementation. Buyers should also budget for model inference, their own infrastructure, security review, and ongoing operations.
What happened in the Context.ai security incident?
Vercel's April 2026 security bulletin says a compromise of Context.ai allowed an attacker to take over a Vercel employee's Google Workspace account and pivot into Vercel systems. Organizations evaluating the current platform should review the incident report, remediation evidence, penetration-test results, OAuth controls, credential handling, and architecture of their exact deployment.
Does Context use customer work to train shared models?
Context says it does not use one customer's traces, corrections, or institutional context to train models for other customers. Retention and training policies at the selected model provider still need to be documented and verified.
Bottom line
Our Context verdict
Context is designed for a harder problem than a general office copilot: governed, repeatable agents operating across production systems and sensitive data. Its runbooks, evaluations, model choice, and customer-controlled deployment options are compelling for mature enterprises. The tradeoff is equally significant—custom pricing, operational complexity, and a security history that warrants a rigorous, evidence-based review before granting access.
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