How Enterprises Govern AI Agents: Practices That Work in Production

AI agent failures in production typically stem from missing engineering controls rather than model intelligence. Non-deterministic agents can cause duplicate tasks, budget exhaustion, and incorrect data mutations. Production-ready systems require infrastructure control planes, scoped virtual keys, and runtime guardrails. Tools like Bifrost and Model Context Protocol (MCP) enhance governance. Frameworks like Laravel support reliability by providing essential primitives such as queues, validation, and rate limiting, transforming agents from simple chat prompts into supervised workflows.