Explores how large language models (LLMs) can enhance functionality by engaging with external systems through function calls. The post discusses methods for implementing AI agents using Python that interpret user intent, select actions, and execute them using APIs while emphasizing security measures like input sanitization to guard against prompt injections. It also compares LLM-based systems with traditional rules engines, illustrating the flexibility and adaptive capabilities of LLMs. Additional tools like MCP are introduced for dynamic tool discovery and interaction.
Table of contents
Scaffold of a typical agentUnit testsSystem promptRestricting the agent's action spaceGuardrails against prompt injectionsAction classesRefactoring to reduce boiler plateCan this pattern replace traditional rules engines?Function calling vs Tool callingHow Function calling relates to MCP ( Model Context Protocol )ConclusionSort: