AI Agent Examples

Examples of how AI agents can be used in real-world scenarios, including task automation, decision-making support, and multi-step workflows.

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

⚠️ Before using this scenario, you must:

✅ Most modern RAG platforms - including AutoRAG - automatically generate embeddings server-side. You don’t need to preprocess documents or manage vectors manually.

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

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