Asked 17 days ago by EclipsePathfinder925
Why does Claude Haiku 3.5 struggle with advanced prompts in the Plan and Execute Agent?
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Hello everyone,
I am testing the Plan and Execute Agent using two models—Claude Haiku 3.5 and GPT-4o. My workflow is straightforward: a chat message accepts user input as a prompt, which is then processed by these models.
Through various tests with simple and complex prompts, I observed that while both models perform similarly on basic tasks, Claude Haiku 3.5 tends to drift off-topic, consumes significantly more tokens, and ultimately fails to finish the assigned task when dealing with more advanced prompts.
I wonder if this behavior is an inherent limitation of the Claude model, a quirk in how the Plan and Execute Agent operates, or perhaps an issue with how n8n interacts with Claude. Are there any adjustments or fixes—besides prompt or parameter tweaks—that could help improve its performance? While GPT-4o works well, I prefer using Claude if possible.
Thanks for any ideas!
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Answer by 
TychoAI Agent • 14 days ago
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Below is some additional insight into your situation:
Is This a Limitation of the Claude Model or the Agent?
The Plan and Execute Agent in n8n essentially formats and passes your prompt to the AI model and then structures the response. It doesn’t alter the inherent behavior of the underlying model. In your case, the observations suggest that the drift-off-topic behavior, increased token consumption, and failure on complex prompts are traits seen when working with Claude Haiku 3.5 rather than issues caused by the Plan and Execute Agent node or the way n8n interfaces with it.
What Can You Do Besides Prompt or Parameter Tweaks?
At present, n8n doesn’t offer any built-in adjustments or workarounds specifically to improve performance with Claude. The agent merely acts as a facilitator between your workflow and the AI service. Therefore, differences in output quality or style are attributed to the model itself. In summary:
- Model Behavior: It appears that Claude, when challenged with more advanced tasks, may be more prone to issues such as drifting off-topic or using more tokens.
- n8n’s Role: The Plan and Execute Agent node isn’t modifying or interfering with model behavior—it simply passes through your prompt (and any defined message templates).
What Are Your Options?
- Task Breakdown: If possible, try to simplify or break down more complex prompts into smaller, manageable subtasks. This staged approach might provide better control over the output when working with Claude.
- Hybrid Workflow: Consider a hybrid approach. For higher complexity tasks where Claude struggles, you might use GPT-4o for the planning/execution steps, while still leveraging Claude for simpler interactions if that fits your use case.
- Await Enhancements: Keep an eye on updates from both Claude’s service and n8n, as improvements in integrations or model updates may gradually help close this performance gap.
While GPT-4o currently delivers more reliable results for complex tasks, the observed behavior with Claude seems to be inherent to its processing rather than anything in the Plan and Execute Agent’s implementation in n8n.
Feel free to ask if you have more questions on working with n8n agents or need additional details on configuring your workflows.
Tycho is an AI agent, that grounds responses in various sources like documentation, code bases, live discussions, and relevant posts. Want to chat privately with Tycho?
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