How Does n8n Work Under the Hood? (With AI Agents, Tools, and Memory)

What Is an n8n AI Agent?

1. An n8n AI Agent combines:

• A Large Language Model (LLM) for reasoning and decision-making.
• A set of tools (integrations with other apps and services).
• A memory component to retain context between tasks.

Together, this enables the agent to understand goals, make decisions, access data, and complete tasks — all in a node-based visual workflow.

2. Core Components of an AI Agent in n8n:

• Workflow Editor (Build)

n8n provides a visual, drag-and-drop workflow editor where you can connect different services as nodes. This is the canvas where your automation logic lives.

3. AI Agent Node – (Think)

This node connects to an LLM like OpenAI, Anthropic, or any other model. It’s the “brain” of your workflow — capable of:

• Understanding goals
• Reasoning through steps
• Making decisions

4. Tools – (Act)

Tools are external service integrations (like web search, email, APIs, or databases) that the agent can interact with to perform real-world actions or retrieve information.

Examples

• Search Google
• Access a SQL database
• Send an email

5. Memory – (Retain Context)

n8n’s memory node allows agents to remember previous interactions and store relevant data between runs. This helps in:

• Maintaining conversation history
• Contextual decision-making
• Reducing redundant actions

6. How an AI Agent Works (Step-by-Step)

Here’s a simplified flow of how an agent operates inside n8n:

• Trigger (Sense) : Every workflow starts with a trigger — it could be a schedule, webhook, or form submission.
• Reasoning & Decision Making: The AI Agent node processes the input, understands the goal, and analyzes the available tools to decide the next action.
• Tool Selection & Execution: Based on its reasoning, the agent selects the right tool(s) and performs an action (e.g., fetching user data, querying an API, etc.).
• Feedback & Next Steps: The agent evaluates the results, stores useful info in memory (if needed), and plans the next step or ends the process if the goal is met. This loop continues until the desired outcome is achieved.

7. Key Features and Benefits of n8n AI Agents

• Autonomous Operation: Agents can perform multi-step workflows without manual intervention, saving time and effort.
• Dynamic Adaptability: They can adapt their behavior in real-time, depending on context and external data.
• Low-Code AI Automation: You don’t need deep coding knowledge — just use the visual interface to build advanced logic with AI-powered automation.

8. Real-World Example Use Cases

• Automate customer support by integrating email, web search, and AI response generation.
• Build CRM updaters that read customer messages and enrich the database.
• Use AI to summarize reports, update dashboards, or send notifications.