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The latest n8n update brought three important features to AI Agent, a node that allows you to integrate artificial intelligence into workflows. Developers and automators can now:
- Configure the Fallback Model in case of a basic error,
- Select a model for a specific task (Model Selector),
- Create chains of multiple AI Agents for complex scenarios.
Let's take a closer look at each of these features.
1. Fallback Model: a backup model in case of an error
The problem:
Previously, if the main model (for example, OpenAI GPT-4) gave an error or was not available, the whole process could be interrupted.
Solution:
You can now specify a Fallback Model in the AI Agent. If the first model fails, n8n will automatically switch to a backup option (for example, Anthropic Claude or local Llama 3).
Example of use:
- Main model: GPT-4-Turbo (fast and accurate, but expensive)
- Fallback Model: Mixtral 8x7B (less expensive, but slightly slower).
If GPT-4 does not respond for some reason, the request will automatically go to Mixtral and the workflow will not stop.
2. Model Selector: choosing a model for a specific task
The problem:
Different tasks require different models. For example:
- GPT-4 is better suited for text analysis.
- Claude 3 Opus for code generation.
- For cheap mass requests — Mistral 7B.
Previously, you had to manually switch models or create difficult conditions.
Solution:
AI Agent now has Model Selector, a tool that allows you to set the conditions for selecting a model.
Example of use:
If (the request contains “code”) → use Claude 3.
Otherwise, if (the query is complex) → use GPT-4.
Otherwise → use Mistral.
This allows us to optimize costs and improve the quality of responses.
3. AI Agent Chains: Connecting Agents to Each Other
The problem:
Complex tasks (for example, analysis → refinement → verification) required several separate nodes, which made workflow more complicated.
Solution:
AI Agent can now be connected to another AI Agent as a tool. This allows you to create sequential processing chains.
Example of use:
1. The first agent (GPT-4) analyzes the text.
2. The second agent (Claude 3) tests the logic.
3. A third agent (Llama 3) formats the response.
Thus, it is possible to build complex AI pipelines right inside the n8n.
Conclusion: powerful AI Orchestration in n8n
These three innovations make AI Agent an even more flexible tool for:
- Fallback Model,
- Cost and quality optimization (Model Selector),
- Creating complex AI chains (embedded agents).
With this update, n8n is becoming a powerful tool for AI orchestration, allowing you to combine different models and approaches in one workflow.