Connecting an LLM to external systems has converged on an established set of open protocols that standardize communication between autonomous agents and enterprise infrastructure. However, as organizations move beyond chat interfaces into deploying multi-user production agents, a new bottleneck has emerged.
Protocol connectivity is largely solved, with MCP established as the de facto connectivity standard and native support across clients like Claude Code, Cursor, and Windsurf. What limits agent scaling now is tool quality, including context efficiency, multi-user authorization, lack of audits, and execution reliability.
When you're building AI architectures, you must evaluate the protocol layer (the complementary roles of raw function calling, the Model Context Protocol, and agent-to-agent coordination). Alongside that sits tool sourcing, which determines where your capabilities come from and whether they're optimized for deterministic software or autonomous reasoning.
TL;DR
Function calling is the LLM's native ability to emit structured JSON for a tool.
