Enterprises face a growing challenge when trying to scale AI agents securely across diverse systems. As organizations adopt agentic workflows and connect large language models (LLMs) to internal tools such as Jira, Confluence, GitHub, or in-house APIs, risks increase quickly. Integration sprawl, security blind spots, inconsistent access controls, and sudden cost spikes can turn early productivity gains into operational liabilities.
Key concerns often arise. Who authorized an agent to close production tickets? Is sensitive data being exposed through prompts? Which team is responsible for a surge in token usage? These questions illustrate why control, visibility, and governance are now essential for enterprise AI adoption.
TrueFoundry’s MCP Gateway addresses these challenges directly. The platform extends the open Model Context Protocol (MCP), introduced by Anthropic in late 2024, into a comprehensive AI Gateway that acts as a single point of orchestration, governance, and visibility.
TrueFoundry builds on MCP with capabilities such as discovery of models, tools, and agents; granular role-based access control; unified observability into all agent-tool interactions; and tracking of token usage and costs. “At TrueFoundry, we see a future where AI agents will operate across hundreds of tools and workflows — but it has to be done responsibly,” says Nikunj Bajaj, co-founder and CEO of TrueFoundry. “The MCP Gateway ensures every interaction is visible, governed, and secure. It brings the clarity and control that enterprises need to unlock the full potential of agentic AI without letting complexity run wild.”
A defining feature of our approach is the integration of MCP servers with TrueFoundry AI Gateway.
This creates a single, cohesive layer for managing both model access and tool execution. Through this unified interface, enterprises can route LLM traffic from providers such as OpenAI, Anthropic, or self-hosted models. Built-in features include rate limits, budget enforcement, key rotation, and response monitoring, enabling fine-grained control over how models are used and what they return.
In parallel, the MCP Gateway manages the actions models take, ensuring every tool invocation is authenticated, audited, and policy-compliant. This gives organizations full visibility across the lifecycle of an AI agent’s decision-making process.
The focus now is on enabling AI agents to act. With the MCP Gateway, enterprises can connect agents to internal tools such as GitHub, Jira, Slack, and custom APIs to automate workflows like incident triage, support-thread summarization, and CRM updates.
With this foundation, TrueFoundry transforms MCP from an experimental standard into a production-ready foundation for enterprise AI. The outcome is connected tools alongside trusted agentic systems capable of moving confidently from pilot projects into production.
Agentic AI is here, and with TrueFoundry’s MCP Gateway, it is ready for enterprise scale.
