The common "start small" approach creates a sprawl of low-value AI agents without proper governance. Instead, TrueFoundry's Nikunj Bajaj advises focusing on five critical, high-impact workflows. This justifies building a robust, scalable infrastructure from the outset, which can later support smaller initiatives and ensure success.
There's an optimal stage for startup innovation. Companies are large enough for diverse customer feedback but small enough that product leaders are still interacting directly with clients. This tight feedback loop, where decision-makers hear problems firsthand, allows them to innovate faster than tiny startups (not enough data) or large corporations (too much bureaucracy).
Many companies initially build their own AI gateway, viewing it as a simple, thin proxy layer. However, upon moving agents to production, they quickly discover that real-world complexity around governance, observability, and security requires a far more robust, specialized control plane platform.
Unlike model gateways managing simple API keys, tool (MCP) gateways handle greater complexity. They must interface with diverse authentication methods for different tools (e.g., Slack, Gmail) and manage granular read/write permissions to prevent autonomous agents from taking unintended actions with sensitive data.
A comprehensive AI management system requires more than just an LLM router. It needs three distinct gateways: a Model Gateway for controlling LLM access, an MCP Gateway for secure tool and data interaction, and an Agent Gateway to govern communication between different autonomous agents and provide a "kill switch."
TrueFoundry positions its platform as a control plane between applications and infrastructure. Its core functions are captured by the memorable "COG" framework: providing a single place to Connect to models and tools, Observe all AI interactions, and Govern access, costs, and behavior for enterprise agents.