Agent Control

Agent Control provides a unified control plane to manage and govern AI agent behavior in production. It lets teams set policies, enforce safety rules, and monitor agent activity across diverse deployments. Built for engineering and DevOps teams, it integrates with existing agent frameworks and infrastructure to offer real-time oversight without modifying agent code. The platform works by intercepting agent runs and applying configurable policy checks, logging, and observability. Users define guardrails via a declarative configuration language or UI, covering areas like output validation, rate limiting, sensitive data handling, and cost tracking. This enables consistent governance across thousands of simultaneous agent executions. Agent Control is designed for scale: it handles high-throughput deployments with minimal latency overhead, and its extensible architecture supports custom plugins for business-specific rules. It positions itself as essential for regulated industries or any organization that needs accountability in autonomous AI workflows. Unlike standalone monitoring tools, Agent Control focuses on proactive enforcement, not just logging. It's a production-first solution that prioritizes reliability and policy compliance, making it distinct from academic or prototype-level guardrail frameworks.