Archestra is an enterprise-grade, open-source platform designed for organizations to safely build, manage, and scale AI agents. Created by a team with extensive experience in observability and systems engineering, Archestra provides a centralized infrastructure layer that bridges the gap between technical teams building sophisticated agents and non-technical staff utilizing AI for daily productivity. The platform is designed to be self-hosted, allowing organizations to retain control over their data, credentials, and security policies while leveraging the Model Context Protocol (MCP) to integrate disparate internal and external tools.

Functionality: The platform serves as an agentic gateway that orchestrates AI operations through a unified interface. It manages the entire lifecycle of AI agents, from development and testing to deployment and production monitoring, while enforcing security guardrails, cost controls, and role-based access management across an organization's internal infrastructure.

Some of the key features are:

• Agentic Chat: A secure, enterprise-ready chat interface for all users with SSO authentication and per-user identity enforcement.
• MCP Orchestrator: Runs MCP servers as isolated containers in Kubernetes with human-in-the-loop approval workflows for security and governance.
• Security Guardrails: Deterministic tool invocation policies that prevent data exfiltration by marking sensitive conversations as tainted.
• Observability & Cost Tracking: End-to-end tracing via OpenTelemetry with granular per-user, per-team, and per-agent cost attribution.
• Knowledge Base: Built-in RAG capabilities that respect source system access control lists to ensure users only query what they are authorized to see.
• Code Sandbox: Secure, containerized execution environment for agent-generated code that keeps processing off user machines.
• LLM Proxy: A centralized endpoint for managing multiple LLM providers, virtualizing API keys, and handling complex OAuth flows.

Operation: Archestra functions as a modular platform that integrates with existing organizational tools. Deployment is typically handled via Docker or Kubernetes. Administrators configure environments, integrate identity providers for SSO, and approve MCP servers through the orchestration layer. Once set up, users interact with agents via the built-in Chat interface or integrations like Slack and MS Teams. Developers can migrate existing agentic workflows to Archestra's runtime to take advantage of its security and observability features.

Some common use cases include:

• Internal Agent Scaling: Deploying AI agents to 1,000+ employees while maintaining strict security oversight and preventing runaway API costs.
• Secure Data Access: Enabling AI agents to query sensitive internal documentation (like Jira or Confluence) while automatically filtering results based on existing user permissions.
• Collaborative Engineering: Using shared project workspaces where multiple team members and agents interact with the same files and context.
• Productionizing Prototypes: Moving agents built in development tools like Claude Code into a managed production environment with standardized logging and access policies.