Decawork

Companies are deploying AI agents across functions like recruiting, IT, finance, legal and engineering, each with access to tools and data via API and MCP connections. Whether a given agent action is appropriate depends on context: the same action can be correct for one task and wrong for another, and that context shifts as the agent works. Without a way to continuously check agent actions against company policy, organizations risk agents taking actions that are misaligned with their interests, while manually reviewing every action would be too slow to let agents actually work.

Decawork connects to a company's AI agents by routing their API and MCP tool calls through its platform, giving visibility into every agent, its owner, and its activity in one registry. Companies set policies in plain English describing their interests and when a human must step in, and Decawork trains a company-specific model that evaluates each proposed agent action against those policies and the current task before allowing it to run. The system reassesses access as agents work through a task and revokes it once the task is done, rather than granting static, always-on permissions. The product includes an agent registry, live approvals, a policy editor, and integrations, shown in the walkthrough handling tasks like onboarding, candidate briefing, contract review, and invoice reconciliation.