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Agentic AI refers to a language model executing multi-step tasks toward a goal — using tools, planning intermediate steps and evaluating results — instead of just answering a single prompt. This guide explains the core concepts and sets up a self-hosted agent workflow using n8n and Ollama.
For a system to count as an "agent," four core capabilities need to be present together.
The model being able to use external tools like calling an API, reading a file or running a command, instead of only generating text.
Breaking a complex goal into smaller steps that are executed sequentially or conditionally.
Storing prior steps and results so they can inform the next decision.
The agent checking its own output and planning a new step if the goal hasn't been reached yet.
A chatbot takes one prompt and produces one response; an agent carries out multiple steps on its own until the goal is reached.
Chained operations like "pull data from this API, process it, send an email" are carried out through steps the agent plans itself, not a single prompt.
Instead of just generating text, it can make real API calls, run database queries or perform file operations.
When a step fails, the agent can evaluate the result and try an alternative path.
As autonomy increases, which tools the agent can access and which actions it can take without approval need to be constrained.
n8n's AI Agent node lets you combine a language model with tools to build agent logic in a visual interface.
Spin up n8n and Ollama, which runs a local language model, on your server with Docker.
Define an Ollama Chat Model credential inside n8n to connect to the language model.
Add the AI Agent node to your workflow and write a system prompt defining the goal.
Connect HTTP Request, code execution or other n8n nodes as tools the agent can use.
Add a memory component so the agent can recall previous messages.
Test it with a real task; limit the tools the agent can access and the number of steps for safety.
curl -fsSL https://ollama.com/install.sh | sh ollama pull qwen3:4b
docker run -d --name n8n -p 5678:5678 -v n8n_data:/home/node/.n8n n8nio/n8n
Turkey (Turkish)
Germany (German)
Worldwide (English)
curl http://localhost:11434/api/generate -d '{"model":"qwen3:4b","prompt":"hello"}'docker logs -f n8n
GET https://api.example.com/status -> connected to the AI Agent node as a Tool
docker restart n8n ollama
No. RAG is the model pulling relevant content from an external knowledge source and adding it to context while generating a response; an agent goes further, involving multi-step planning and tool use. An agent can use RAG as one of its tools.
No. Agent logic can also be built on a local/self-hosted model using tools like Ollama; model quality and speed depend on your hardware.
Limit tool access with least-privilege, add a human-approval step for irreversible actions (deletion, payments, etc.), and cap the number of operations/steps.
Libraries like LangChain and LlamaIndex let you build agents in code; tools like n8n and Flowise enable agent construction through visual workflows.
No. Agents still carry the limitations of the underlying language model; hallucination and incorrect tool selection are risks, so verification steps matter for critical operations.
Yes. Both services can be installed together via Docker on a Linux VPS with sufficient RAM and CPU/GPU resources; resource needs vary based on the chosen model size.
Check out our Linux VPS plans, ready for your AI agent workflows with NVMe storage and full root access.