What it does
Prediction Guard describes itself as a self-hosted AI control plane for regulated enterprises. It runs inside your own infrastructure beside your agents, and is meant to control what agents can access, enforce policies while they run, and let you investigate and audit their actions across a fleet. The platform has three parts. Agent Forge deploys interactive and autonomous agents from an API or a natural-language prompt, with key systems connected through MCP and human and agent identities tracked. An API and SDK layer compatible with OpenAI, Anthropic and MCP sits on top of the control plane and works with self-hosted or cloud endpoint models and with tools such as LangGraph, Pydantic AI, Crew AI, Claude Code and the OpenAI SDKs. The control plane itself covers supply chain, runtime controls, observability and evidence, and token and spend enforcement. The site also lists a no-code interface for building agents and says a verified Agent Control Kit has been released on Docker Hub. Visitors are directed to book a demo, and no prices are given.
Capabilities
- Runtime policy controls that keep agents within defined behaviour
- Control over which systems and tools an agent can access
- Token and spend enforcement across agents
- Investigation and audit of agent actions across a fleet
- OpenAI, Anthropic and MCP compatible API for self-hosted or cloud models
- Agent Forge for creating interactive and autonomous agents
When to use it
- You run AI agents in a regulated or compliance-constrained environment and need them inside your own security boundary
- A platform team needs to limit, monitor and audit agents across an organisation
- A software vendor wants to ship agent-based products into compliance-constrained environments
