Quality output from an LLM is the make-or-break between a task that performs well and a hallucination. The level of accuracy that's output is top of mind for everyone
Architecture diagrams always look something like this:
Agent -> Gateway -> LLM (or MCP Server).
The Agents that organizations are typically referring to are Agents that perform an action via prompts by a
AI traffic that goes through enterprise systems should include everything from servers, cloud environments, and even laptops, desktops, and mobile devices. This level of observability and security isn't "new"
If you're using an Agent that you built, a pre-built Agent (Claude Code, Ollama locally, etc.), or a provider-based Agentic UI (Gemini, ChatGPT, etc.), the question is - how do
The running joke is "The S in MCP stands for security", and for good question. Out of the box, there's realistically no way to secure traffic from a user
