A file analysis agent is one of the simplest yet most broadly useful agents that every business needs. While applications like Notion have file-level summarizers and Google Drive likely implements similar features, having your own file analysis agent provides customized control over your documents.
Why Do We Need a File Analysis Agent?
When you have numerous files—whether product documents, catalogs, FAQs, or manuals—you often need to find specific information. Human memory works with vague concepts rather than systematic file-version-line details. A file analysis agent allows users to express these vague concepts and let the machine locate the relevant information across all files.
Another key use case is enabling external parties to search through your files without requiring manual hand-holding. Users can ask questions in natural terms, and the agent can identify which file, section, or content contains the answer, then provide summaries or references as needed.
For this demonstration, we’ll focus on text files and programming language files since they are predominantly text-based and easy to read. The same principles apply to other file types like Excel or Word documents, though those would require conversion software to extract text content if the AI models cannot read them directly.
What Tools Do We Need?
An AI agent consists of three core components: tools, objectives, and the model. The tools run locally on your computer or cloud server, while the model typically operates through an external provider like OpenAI, Anthropic, or in this case, GLM.
The agent acts as a harness that allows the model to interact with your computer’s files through a specific set of tools, guided by the overall objective. For a file analysis agent, the essential tools are:
- List files: A programmatic way to enumerate all files in a directory
- Read file: Access the contents of specific files
- Grep: Search within file contents
These tools follow the Unix philosophy where each tool performs one specific job. The model communicates with the agent through raw text exchanges, requesting file listings or content as needed to accomplish the analysis task.
The programmatic approach is superior to visual screen reading methods because all operating systems provide direct ways to list files, read content, and search through text. This enables efficient, automated file analysis without the complexity of visual interpretation.
What Tools to Avoid?
For a file analysis agent, the primary tools to avoid are any that modify files or directories. Since the agent’s purpose is to read and analyze content rather than make changes, you should exclude:
- Write file permissions
- File modification tools
- File creation tools
- Folder creation tools
- File or folder deletion tools
The model can only perform actions that the agent’s toolset allows. If the agent doesn’t provide write file tools, the model cannot write files regardless of its capabilities. This isolation ensures the agent stays focused on its analysis purpose and prevents unintended modifications.
A recommended approach is to create multiple small, specialized agents rather than one large agent with many capabilities. This allows you to isolate permissions and tools for each agent, making your automation flows more secure and predictable. For example, you might have a separate agent that saves analysis results to files, while the analysis agent remains read-only.
Let’s See It in Action
The code analysis agent demonstrates these principles in action. The system prompt defines the agent as a codebase analysis expert tasked with examining software repositories and providing high-level project descriptions.
The agent operates through a simple loop structure that allows up to 30 iterations between the model and the tools. Each iteration represents one exchange where the model requests information and receives it back.
When we run the agent with the prompt “analyze the structure of this project,” the process unfolds as follows:
- The model requests a list of files with recursive directory exploration
- The agent returns the file structure, revealing this is a Rust project with .rs files and cargo.toml
- The model identifies key files to examine, starting with README.md for project overview
- It systematically reads important files: src/lib.rs, cargo.toml, bin/runner.rs
- The model analyzes the content and builds understanding of the project architecture
- Finally, it provides a comprehensive analysis including technical details, architectural strengths, and code quality assessment
The interaction is straightforward text exchange—no magic involved. The model makes intelligent decisions about which files to read based on their names and the project structure, then synthesizes the information into a useful analysis.
This example shows how a simple agent with basic tools can provide sophisticated file analysis capabilities, making it valuable for understanding codebases, documentation, or any collection of text files.
Conclusion
File analysis agents represent a fundamental building block in the automation landscape. As businesses increasingly automate repetitive tasks, AI-driven agents will handle the boring, repetitive work that can be done more efficiently with artificial intelligence.
Understanding how these agents work—the tools they need, the permissions they should avoid, and how they interact with models—provides a solid foundation for building more complex automation systems. The key is to start simple, focus on specific objectives, and expand capabilities through multiple specialized agents rather than monolithic solutions.
The file analysis agent demonstrates that even with basic tools like list files, read file, and grep, you can create powerful solutions that help users navigate complex information landscapes and find the answers they need without manual searching.