Credal

Credal is an enterprise-grade platform designed to build, register, and deploy AI agents and Model Context Protocol (MCP) servers from a single, unified interface. Its core value proposition is to serve as a centralized agent registry for AI platform teams, effectively eliminating agent sprawl through built-in governance, security, and compliance controls. By providing a structured environment for the entire lifecycle of AI agents, it enables organizations to scale their AI operations securely and efficiently, ensuring that all deployed agents are auditable, secure, and aligned with internal policies.

Key features: The platform offers a comprehensive suite for managing AI agents, including tools for building agents with secure data access, automatic data curation from connected sources, and debugging capabilities. It provides a centralized registry to catalog all agents, complete with version control and deployment pipelines to various environments, including cloud and on-premise. Built-in governance features encompass detailed audit logging of all agent activities, data loss prevention (DLP) policies, and compliance tooling for regulated workflows. Secure integration is a cornerstone, allowing agents to connect safely to internal systems, databases, and APIs while enforcing strict access controls.

What sets Credal apart is its deep integration of security and governance directly into the agent development and deployment workflow, a focus often secondary in other agent platforms. It is technically distinguished by its support for the Model Context Protocol (MCP), facilitating standardized connections to data sources and tools, and its ability to integrate with leading models from providers like Anthropic and Google. The platform is engineered for enterprise IT environments, offering features like single sign-on (SSO), role-based access control (RBAC), and comprehensive audit trails that are essential for large organizations operating in regulated industries.

Ideal for enterprise AI platform teams, security-conscious development groups, and organizations in regulated sectors like finance, healthcare, and professional services that are building internal copilots or custom AI solutions. Specific use cases include deploying secure AI agents for customer support automation, internal data analysis copilots that require access to sensitive information, and compliant workflow automation that must adhere to strict data governance standards. It is particularly valuable for companies experiencing rapid growth in their AI agent deployments and needing to impose order, security, and operational oversight.

Pricing follows a freemium model with a free tier for individual developers or small teams to get started, while enterprise plans with advanced governance, security, and support features are available through custom quotes based on the scale of deployment and required capabilities.