How can I improve my agent's tool-calling accuracy and reasoning capabilities?

Question

My bot keeps tripping over its own feet when calling tools and I'm honestly so frustrated. Im trying to finish this support agent for my sisters online store by next Friday but it keeps hallucinating arguments and just acting weird. My logic was that ReAct would fix the reasoning because it makes it think first but it just gets stuck in loops.

I read that few-shot prompting helps but others say it clutters the context window and makes things worse. My budget is pretty tight so I'm stuck using smaller models for now. Should I be rewriting my tool descriptions or is there some prompt trick to stop it from picking the 'refund' tool when someone just wants to track a package?

mylvwlsmgg · Accepted Answer

Unfortunately ReAct is not as good as expected for reliability. I had issues with loops too. Try:Meta Llama 3 8B Instructclear negative constraints Its way safer for bots tbh.

dqgwuvyvpp · Answer

> my logic was that ReAct would fix the reasoning I had this exact issue last month. Unfortunately, ReAct created more problems than it solved on my budget hardware. I spent weeks fighting those loops only to realize the model wasnt smart enough. It was messy. Moving to Mistral AI Mistral 7B v0.3 8K Context using native function calling saved the project. Just keep tool descriptions super short and blunt.

ydfsttkdtm · Answer

To add to the point above: honestly, tool calling is such a thrill when it finally clicks! I've spent ages banging my head against the wall with loops and hallucinations. The trick with smaller models is that they need structure, not just vibes. ReAct is awesome for big models but it's a total resource hog for the little guys and usually leads to those annoying loops you're seeing. Check out these tips to save your Friday deadline:

Detailed docstrings are everything! Instead of just track_order, name it track_order_status and specifically tell it: Do NOT use this if the customer is asking for a refund. Giving the model a negative example right in the tool description works like magic.

If you can, switch to Mistral AI Mistral 7B v0.3 because it has native function calling support. It's seriously fantastic at following JSON schemas without getting lost in its own thoughts. It is way more reliable for your sister's store!

Use structured output tools like Ollama Local LLM Runner with their latest JSON mode. It forces the model to fill in specific fields. If it tries to hallucinate a reason for a tracking tool that doesn't need one, the schema will basically block it.

Keep your prompts short. If the context gets too messy, the model loses the plot. Try removing the thought step and just ask for the tool call in JSON. Small models love clear instructions over complex reasoning chains. Stick with it! Once you get the descriptions tuned, it feels like magic seeing it pick the right tool every time. Good luck!