Context.ai

Context is a unified platform for building, deploying, and improving AI agents in enterprise environments. It provides a workspace where teams author plain-English workflows, connect to 800+ pre-built tools, and run agents on hosted, VPC, on-prem, or air-gapped infrastructure. Key modules include Workspace for collaborative agent authoring, Engine for durable orchestration, Unify for identity-based permission inheritance, and Evals for continuous quality improvement via rubrics and acceptance testing. Designed for large enterprises with strict compliance needs—financial services, semiconductors, consulting, telecom, public sector—Context supports any model or agent framework (Claude, GPT, Gemini, Kimi, Llama). Agents inherit user permissions from your IdP (Okta, etc.) at every action, and all runs are fully auditable. Recent updates add Applets for generating interactive UIs, dual-record action logging, a graduation ratchet for automated quality gates, connector permissioning, and dynamic permission requests. In internal benchmarks on specialized enterprise workflows, Context achieved 94% task completion on custom rubrics versus 62% for Claude Cowork and 57% for OpenAI Codex. It also claims 40x faster turnaround and 28x lower cost per case. The platform stores accepted outputs as training data for custom models, and routes tasks to the cheapest model that clears quality rubrics, reducing costs over time. What makes Context different is its focus on production-grade execution: runbooks readable by whole teams, durable agents on ephemeral compute, and a learning loop that keeps improvements within your control. Unlike vertical tools like Harvey or Hebbia, Context offers model choice, flexible deployment, and deep enterprise permissioning. It's best for teams that need governance, audit, and continuous improvement at scale.