🤖 AI Agent Design Patterns: Building Intelligent Automation Workflows

What Is an AI Agent?

An AI agent is a software system that uses a Large Language Model (LLM) as its reasoning engine to autonomously complete tasks by deciding which actions to take, executing those actions (via tools, APIs, or code), and iterating based on the results.

Unlike a simple chatbot that responds to one message at a time, an agent can plan multi-step workflows, call external tools, and adapt its approach based on what it discovers along the way.

Core Patterns in Agent Design

1. Prompt Chaining

The simplest pattern — output from one LLM call becomes input to the next. Each step has a focused role: extract, transform, validate, generate. This keeps individual prompts simple and makes the workflow easy to debug.

Use when: The task can be decomposed into a clear linear sequence of well-defined steps.

2. Tool Use (Function Calling)

The LLM is given a set of "tools" (functions it can call) and decides which tool to use based on the current task. Tools can be web search, database queries, code execution, API calls, or file operations.

Modern LLMs like GPT-4 and Claude are trained to use tools reliably, outputting structured JSON that your code executes, then feeding the result back to the model.

3. ReAct (Reason + Act)

The ReAct pattern alternates between reasoning steps ("I need to find the current price") and action steps (calling a price API). This produces more reliable results than asking the model to reason and act simultaneously.

4. Planner-Executor Architecture

A "planner" LLM breaks down a complex task into subtasks and creates an execution plan. One or more "executor" agents then carry out individual subtasks, reporting results back to the planner which decides what to do next.

5. Multi-Agent Orchestration

Multiple specialized agents collaborate on a complex task. For example: a Research Agent gathers information, an Analysis Agent processes it, a Writing Agent drafts the output, and a Review Agent checks quality. An orchestrator coordinates them all.

Practical Considerations

• Error handling — agents must handle tool failures gracefully and know when to ask for human input
• Context management — long workflows can exceed LLM context windows; build in summarization steps
• Cost control — each LLM call costs tokens; design agents to minimize unnecessary calls
• Observability — log all steps, tool calls, and reasoning so you can debug failures

Building AI Agents on Mobile

Apps like AI Automation Workflow Builder bring visual agent building to your iPhone — letting you design, connect, and run AI workflow nodes without writing code. This is ideal for professionals who want to automate repetitive tasks using AI without deep programming knowledge.