AI Architecture
Function Calling & Tool Use: Connecting LLMs to External APIs and Databases
Architect robust tool-calling loops that empower LLMs to safely query SQL databases, fetch live weather, or trigger transactional webhooks.
The Core Tool Calling Loop
The model receives tool definitions as JSON schemas, determines when to invoke a tool, returns parameters, receives execution output from the backend, and synthesizes the final user answer.
Related Technical Guides
Deepen your understanding with these closely related production architectures and tutorials:
2026 Frontier Stack: LangGraph Checkpointing, Mem0 Scoped Memory & Prefix Caching FinOps
Architect enterprise AI agents with decoupled lifecycles: prompt KV-cache optimization (90% savings), LangGraph state persistence, and Mem0 long-term memory.
Model Context Protocol (MCP) Guide: Connecting LLMs to Local Databases and Tools
Learn the open-source Model Context Protocol (MCP) standard created by Anthropic and how it turns LLMs into extensible agents connected to your infrastructure.
Prompt Caching Architecture: Slashing LLM API Costs and Latency by 90% via KV-Cache Reuse
Master Anthropic and Gemini Prompt Caching to slash API bills and reduce latency on long documents, system instructions, and multi-turn chats.