Decawork

Product Introduction

1. Definition: Decawork is an enterprise-grade AI agent control plane and governance platform for IT and security teams. It functions as a centralized management layer that ingests, secures, and monitors AI agents built by employees across various development tools.
2. Core Value Proposition: Decawork exists to solve the critical problem of AI agent sprawl and ungoverned automation within organizations. It enables companies to embrace bottom-up AI innovation by allowing teams to build agents in their preferred tools (like Claude Code, Cursor, or OpenAI's SDK) while giving IT centralized control over security, access, and compliance. Its primary value is providing a single pane of glass for agent lifecycle management, turning ad-hoc AI scripts into auditable, company-controlled assets.

Main Features

1. Unified Agent Inventory & Registry: This feature provides IT with a real-time, centralized catalog of all AI agents running across the organization. It automatically discovers and registers agents built in disparate tools, tagging each with metadata such as owner, team, status (live/paused), last active time, and source platform (e.g., Claude Code, n8n, LangGraph). How it works: Decawork uses lightweight SDKs and API connectors to integrate with agent development frameworks, pulling metadata into a unified registry dashboard for complete visibility.
2. Granular Access Control & Credential Management: This security-centric feature allows IT to provision and enforce least-privilege access for AI agents. Instead of agents using broad, static API keys, Decawork issues short-lived, scoped credentials and acts as a policy enforcement point. How it works: IT defines access policies (which systems an agent can reach and what actions it can perform) within Decawork. The platform then brokers all agent-tool interactions, injecting temporary credentials and ensuring actions comply with predefined approval gates and permissions.
3. Comprehensive Audit Trail & Action Attribution: This compliance feature logs every action taken by every managed AI agent, creating an immutable record for security and operational review. It answers the critical questions of "what happened, which agent did it, and who approved it." How it works: All agent executions are routed through Decawork's control plane, which logs the timestamp, agent identity, target system, action performed, and the human-in-the-loop approval (if required) before storing it in a secure, queryable audit log.

Problems Solved

1. Pain Point: Unmanaged "Shadow AI" agent proliferation, where employees deploy useful but unvetted automation scripts that pose significant security, compliance, and operational risks due to uncontrolled access to internal systems and data.
2. Target Audience: Primary users are IT Operations Managers, CISOs, and Security Engineers responsible for governance. Secondary beneficiaries are department heads (VP of Marketing, Head of Recruiting, CFO) who build agents but need them to be secure and compliant.
3. Use Cases: Essential for scenarios like a marketing team's LinkedIn Content Agent needing access to social media APIs; a recruiting team's Candidate Briefing Agent pulling data from the HRIS; or a finance team's Invoice Reconciliation Agent interacting with accounting software. In each case, Decawork ensures the agent has only the necessary, approved access and its actions are fully logged.

Unique Advantages

1. Differentiation: Unlike siloed AI development platforms (e.g., just using OpenAI) or generic workflow tools (like Zapier), Decawork is tool-agnostic and focused exclusively on post-build governance. It doesn't replace building tools; it sits above them as a mandatory control layer. Compared to traditional IT asset management, it understands the dynamic, API-driven nature of AI agents.
2. Key Innovation: Its core innovation is the conceptual treatment of an AI agent as a "new type of employee" that requires an identity, managed credentials, and an audit trail. The platform's ability to ingest agents from any framework (Claude Code, Microsoft Copilot, CrewAI, custom stacks) and instantly apply enterprise security policies to them is a unique technical approach to the governance gap.

Frequently Asked Questions (FAQ)

1. What is an AI agent control plane? An AI agent control plane is a centralized software platform, like Decawork, that provides governance, security, and operational management for multiple autonomous AI agents across an organization, similar to how Kubernetes manages containers.
2. How does Decawork improve AI security for enterprises? Decawork improves enterprise AI security by eliminating hard-coded credentials in agent code, enforcing granular access policies, mandating approval for sensitive actions, and providing a complete audit trail for all agent activity, which is essential for SOC 2 compliance.
3. Can Decawork manage AI agents built on any platform? Yes, Decawork is designed as a platform-agnostic control plane. It can manage AI agents built on popular platforms including Claude Code, OpenAI Agents SDK, Microsoft Copilot Studio, LangGraph, CrewAI, as well as custom-coded agents, consolidating them into one management interface.
4. What is the difference between an AI agent and a traditional workflow automation? While both automate tasks, an AI agent uses reasoning models (LLMs) to make decisions, handle unstructured data, and adapt its path based on context. Decawork specifically governs these dynamic, LLM-powered agents, which pose unique security challenges compared to deterministic workflow bots.