AI Agent Examples

Latenode's AI Agent is flexible enough to support multiple architectures: from a single dynamic assistant to modular multi-agent systems and external knowledge integration.

1. AI Agent Basic Workflow Example

A single AI Agent receives user prompts and decides which tools to use, if any. This setup is lightweight but powerful � capable of parsing, routing, and composing responses dynamically.

Scenario Structure

• One central AI Agent
• Connected to:
  • weather_tool (e.g. wttr.in)
  • exchangerate_tool (e.g. exchangerate.host)
  • web_search_tool (e.g. factual search)
• Input from Trigger, output to SetVariables

Call Example 1 � Weather + Currency

Prompt:

�What�s the weather in Berlin and how much is 100 EUR in USD?�

• The agent triggers:
  • weather_tool with the city "Berlin"
  • exchangerate_tool for EUR to USD
• Skips unrelated tools

Expected output:

It's currently 17�C in Berlin. 100 EUR is about 108 USD.

Call Example 2 � Simple Fact

Prompt:

�Who is the CEO of Apple?�

• Agent skips weather and currency
• Only triggers web_search_tool

Expected output:

The CEO of Apple is Tim Cook.

?? This scenario is great for lightweight assistants that respond contextually without complex logic trees.

?? You can copy this template here: AI Agent Basic Workflow Example

2. Multiagent - AI Multi-Agent Interaction Example

This approach uses a main agent to break down user requests and forward sub-tasks to specialized agents. Each sub-agent operates independently and can have its own API logic.

Scenario Structure

• main_agent controls the overall logic
• Delegates to:
  • weather_agent
  • finance_agent
  • web_search_tool
• Each sub-agent is connected to dedicated APIs or logic blocks

Call Example 1 � Weather + BTC

Prompt:

�What�s the weather in Tokyo and what�s the BTC price?�

• main_agent sends:

  • Weather part > weather_agent
  • Bitcoin price part > finance_agent

  

Expected output:

The current weather in Tokyo is 27�C and sunny. The current price of Bitcoin (BTC) is $119,218 USD.

Call Example 2 � CEO + HQ Weather

Prompt:

�Who is the CEO of Apple and what�s the weather like at their HQ?�

• Agent parses:

  • Apple HQ location > via web_search_tool
  • Weather in that location > via weather_agent

  

Expected output:

The CEO of Apple is Tim Cook. He has held this position since August 2011, succeeding Steve Jobs.

As for the weather at Apple�s headquarters in Cupertino, California, it is currently 27�C and sunny.

?? This pattern fits well for scalable assistants, where logic needs to be cleanly split.

?? You can copy this template here: AI Multi-Agent Interaction Example

3. AI Agent with Cloudflare AutoRAG Database

Integrate an AI Agent with Cloudflare AutoRAG to retrieve structured external knowledge � such as product documentation, policies, or internal data.

Scenario Structure

• cloudflare_rag_agent handles free-form prompts
• Two HTTP tools connected:
  • rag_database_docs � deep semantic retrieval
  • raw_data � fast, factual lookups

Call Example 1 � Documentation Question

Prompt:

�How does the billing system of Cloudflare work?�

• Agent detects it�s a high-level question
• Selects rag_database_docs to retrieve semantic context
• Responds based on indexed content

Expected output:

Cloudflare's billing system uses a monthly subscription model with pro-rated charges...

Call Example 2 � Quick Data Point

Prompt:

�What�s the max bandwidth on the free Cloudflare plan?�

• The agent determines it's a factual request
• Selects raw_data for direct value retrieval

Expected output:

The free Cloudflare plan includes up to 1 TB of monthly bandwidth.

?? Use AutoRAG-style integrations for assistants that can reason over your documents and give context-aware, accurate replies � without hosting your own vector database or embeddings pipeline.

?? You can copy this template here: AI Agent with Cloudflare AutoRAG Database

Best Practices

• Name all nodes descriptively � they become visible "tools" to the agent
• Use Agent ID to maintain session-based memory
• Set Max Iterations to prevent loops
• Use Output JSON Schema if the response needs to be structured