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
Accuracy and quality of output from an Agent is the make or break between what's truly usable across the enterprise and what's simply a toy. Aside from taking the
Managing various LLM provider accounts, subscriptions, and cost can get cumbersome for many organizations in a world where multiple LLMs are used. To avoid this, you can use what can be called 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 you
