AI Agents

Loop & Agentic Engineering

Move beyond single prompts into self-correcting loops and multi-step agents: critique-revise cycles, the ReAct pattern, tool use, and knowing when to stop a loop.

What Are AI Agents? The Loop, the Limits, and the Hype

An AI agent plans and acts through tools in a loop rather than producing a single response. Here is how the ReAct pattern works, why agents fail, and what actually works in production today.

Function calling

Function calling lets a model request that your code run a specific function with specific arguments. You expose tool definitions; the model returns a structured call, your code executes it, and the result goes back into the conversation.

AI agent

An AI agent is a system where a model plans and takes actions through tools in a loop, rather than producing a single response. It decides what to do next, does it, observes the result, and continues until the goal is met.

ReAct pattern

ReAct (Reason + Act) is the core agent loop: the model reasons about what to do, takes an action via a tool, observes the result, then reasons again. It repeats until it can answer.

MCP (Model Context Protocol)

MCP is an open standard for connecting AI models to external tools and data sources. It replaces bespoke per-integration code with one protocol any compatible client can use.

Tool use

Tool use is a model invoking external capabilities — search, code execution, database queries, APIs — to do things it cannot do from parameters alone, such as fetching current data or performing exact arithmetic.

Guardrails

Guardrails are the constraints that keep an AI system inside acceptable behaviour — input validation, output filtering, topic restrictions, action limits and human approval gates.

Human in the loop

Human in the loop means inserting a person at decision points in an automated workflow — typically to approve consequential actions or review low-confidence outputs before they take effect.