ResearchPilot
An AI agent that supports multi-tool invocation and can autonomously choose among web search, a calculator and a Python code executor to answer multi-step questions.
Timeline (last 90 days)
ResearchPilot is an AI Agent that can autonomously choose among three tools—web search, calculator, and Python code executor—to answer multi-step questions.
The author deployed ResearchPilot online using the free Groq API and Vercel.
The author wrote 25 test cases covering no-tool Q&A, calculation, code execution, search, multi-step tasks, and failure scenarios.
The conditions for a test case to pass include: the Agent used the correct tool combination, did not crash, did not exceed the tool-call budget, was factually accurate, and the automatic judge gave at least 4 points out of 5.
The first run of the 25 test cases resulted in 22 passes, an accuracy of 88%.
The author's countermeasures for the time and token constraints include: setting a 40-second time budget, compressing search results into three short entries, streamlining the prompt, and configuring a backup model to take over when the primary model is saturated.
A public Agent application means anyone who gets the link can trigger code execution, so the code executor must be strictly restricted; the author allows only safe operations and ensures the runtime environment cannot access keys, but admits it is not a perfect sandbox.
To fix the tool-calling problems, the author adopted a three-layer approach: automatic retries with fine-tuned parameters, making tools return clear error messages instead of crashing outright, and explicitly specifying in the prompt that each tool accepts only one parameter.
The author handled the empty-answer problem by having the Agent actively ask the model to continue generating, and triggering a retry if that still fails.
All Facts (9)
ResearchPilot is an AI Agent that can autonomously choose among three tools—web search, calculator, and Python code executor—to answer multi-step questions.
50%UnverifiedThe author deployed ResearchPilot online using the free Groq API and Vercel.
50%UnverifiedThe author wrote 25 test cases covering no-tool Q&A, calculation, code execution, search, multi-step tasks, and failure scenarios.
50%UnverifiedThe conditions for a test case to pass include: the Agent used the correct tool combination, did not crash, did not exceed the tool-call budget, was factually accurate, and the automatic judge gave at least 4 points out of 5.
50%UnverifiedThe first run of the 25 test cases resulted in 22 passes, an accuracy of 88%.
50%UnverifiedThe author's countermeasures for the time and token constraints include: setting a 40-second time budget, compressing search results into three short entries, streamlining the prompt, and configuring a backup model to take over when the primary model is saturated.
50%UnverifiedA public Agent application means anyone who gets the link can trigger code execution, so the code executor must be strictly restricted; the author allows only safe operations and ensures the runtime environment cannot access keys, but admits it is not a perfect sandbox.
50%UnverifiedTo fix the tool-calling problems, the author adopted a three-layer approach: automatic retries with fine-tuned parameters, making tools return clear error messages instead of crashing outright, and explicitly specifying in the prompt that each tool accepts only one parameter.
50%UnverifiedThe author handled the empty-answer problem by having the Agent actively ask the model to continue generating, and triggering a retry if that still fails.
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