AI Research Assistant

This solution builds an intelligent, automated Research Assistant that will replace the entire manual research workflow with a single, seamless interaction. Instead of performing a dozen manual steps, the user will simply provide a single directive to the agent, such as: Research the topic of CRISPR gene editing.

From that point on, the agent will take over, orchestrating a complete research process autonomously. The agent’s automated plan will be designed to intelligently mimic the steps of a human researcher, but with the speed and efficiency of a machine. It will be responsible for:

• Automatically querying Wikipedia to retrieve a high-level summary.

• Automatically searching the arXiv database to find relevant and recent academic papers.

• Automatically performing a general web search to gather supplementary, real-time context on the topic.

• Automatically synthesizing all the collected information into a single, structured report file.

By automating this sequence, the agent will solve the core problem of inefficiency and context switching, delivering a comprehensive research output in a fraction of the time it would take a human to do so manually.

The Agent's Brain

The core of our agent’s intelligence and planning ability will come from its instruction parameter. For a complex, multi-step task like this, the instruction prompt is not just a simple directive; it is the agent’s master plan. We will craft a detailed, comprehensive prompt that explicitly tells the LLM the exact sequence of steps it must follow to complete the research workflow successfully.

This prompt will instruct the agent to:

• Begin by researching the topic. It should use the wikipedia_tool for general information, the arxiv_tool for academic papers, and the GoogleSearchTool tool for supplementary web-based context.

• Synthesize the findings. After gathering information from all three sources, it must synthesize the content into a single, coherent report.

• Save the result. Finally, it must use the report_writer_tool to save the complete report to a file.

By encoding the entire workflow into the instruction prompt, we are using the LLM’s powerful reasoning and sequencing capabilities to drive the agent’s behavior from start to finish.

The Agent's Tools

To equip our Research Assistant with the necessary capabilities, we will provide it with a set of specialized tools:

• wikipedia_tool: It is a custom Python function we will write that takes a search query as input. It will use a third-party Python library to interact with the Wikipedia library and return a concise summary of the relevant article.

• arxiv_tool: It is a custom Python function that will take a search query. It will use a Python wrapper for the public arXiv API to find the most recent and relevant academic papers, returning their titles and summaries.

• GoogleSearchTool: It is the built-in tool to find supplementary, real-time information from across the web. We will use this powerful, prebuilt tool provided by the ADK framework. This tool enables the agent to perform a Google search and receive a summary of the results.

• report_writer_tool: It is a simple utility function that will take a string of text (the agent’s research notes) and a file name as input. Its job is to write this content to a local file, simulating the creation of the final report.

Features

• Single Command Research: Initiate a complete research workflow with a single directive.
• Multi-Source Data Gathering: Collect information from Wikipedia, arXiv, and general web searches.
• Automated Synthesis: Compile and structure the gathered data into a coherent report.
• Time Efficiency: Drastically reduce the time required for research tasks.
• Contextual Understanding: Mimic human research strategies for effective information retrieval.
• Customizable Output: Generate reports in various formats (e.g., PDF, Word, Markdown).
• Scalability: Handle multiple research topics simultaneously.