Peta

Peta is an infrastructure layer that sits between your AI agents and your tools, APIs, and data systems, providing centralized security, control, and observability. Its primary purpose is to ensure that every tool call made by AI agents is authenticated, governed, and auditable, without requiring extensive custom security logic in each application. By acting as a managed MCP (Model Context Protocol) runtime, Peta standardizes how agents interact with external capabilities across your organization.

Core features include a secure vault for managing and encrypting API keys, credentials, and other sensitive configuration, ensuring that agents never handle raw secrets directly. Peta maintains a detailed tool-call audit trail, capturing who or what invoked which tool, with what parameters, and when, enabling compliance, debugging, and post-incident analysis. Policy-based approvals allow teams to define fine-grained rules for which agents can access which tools, under what conditions, and with optional human-in-the-loop workflows for sensitive operations. Its managed MCP runtime simplifies integrating existing tools and APIs into AI workflows while enforcing consistent security and governance policies.