How Enterprises Use Runtime Control Planes to Govern Autonomous AI Agents

Autonomous AI agents pose unique security risks that traditional API controls cannot address, as they independently select tools, execute multi-step plans, and interact with enterprise systems in non-deterministic ways. Unlike passive chatbots, agents can read, write, and execute commands across databases and cloud infrastructure, creating vulnerabilities such as excessive privilege delegation and prompt injection attacks. To counter these risks, enterprise teams are adopting centralized AI gateways that enforce access limits, model routing, and spend controls at runtime rather than embedding governance logic inside agent code. Bifrost, an open-source AI gateway built in Go by Maxim AI, enforces policies across more than 1,000 models while adding only 11 microseconds of latency at 5,000 requests per second. Governance frameworks also extend to Model Context Protocol settings, restricting which tools and file systems an agent can invoke to prevent unauthorized operations triggered by malicious external content.