In late October 2026 I wrote three LinkedIn‑style posts that compare architectural patterns for tool and decision integration in LLM‑driven agents. The first post contrasts MCP‑based tool calling with native function calling, the second introduces decision models (Jev) versus prompting LLMs for bounded answers, and the third outlines a hybrid Retrieval‑Augmented Generation plus Cache‑Augmented Generation (RAG + CAG) pipeline for cost and latency optimisation.
Function Calling and Tool Use in Agentic Systems are almost the same thing. You can implement tools via functions, the only difference is that functions are usually used to enforce stricter structure to the input and output schema.
In this case your Agent becomes an MCP Host and implements one or more MCP Clients to communicate with MCP Servers.
Jev runs P50 at 0.21 s and P95 at 0.34 s, and OpenAI advertises Decisions API to be close to 150 ms against roughly 1.6 s for the same model answering normally.
Jev is $0.042 per million input tokens with free output.
I decide to use MCP when a fleet of agents must share a common tool catalogue or when tool versioning needs central management, and I prefer native functions for rapid iteration, low‑overhead deployments, or when tool reliability is critical.
I adopt decision models for high‑throughput classification tasks with bounded answer sets, where latency (P50 = 0.21 s, P95 = 0.34 s) and cost ($0.042 / M input tokens) dominate.
I deploy a hybrid RAG + CAG pipeline when I have a mix of stable, rule‑heavy knowledge that can be cached and ever‑changing external information that must be retrieved at query time.