### USER I want to build an LLM agent that leverages tool usage and reasoning in order to act as an Email Triage Agent. I imagine having a list of categories (or tags i guess since I’m on gmail?) that the LLM agent can then sort the emails into their respective folders by using the tools available to them, but I want to make sure that the agent has all the tools it could need in order to get all the information and context about the inbox and its emails, and how to allow it to organize the data in a way that is safe to my data. What tools should I include with the agent? I'll handle the logic of actually implementing everything myself, that part is not too hard, I just want to think on a high abstract level of what tools I should even think about to add to the LLM calls. Lets make a list of tools in a numbered list with a short description for what each tool should do, and what kinda data it should approximately take in and put out ### ASSISTANT
Thought for 11 seconds > Okay, the user wants to build an LLM agent for email triage. They mentioned using tools to gather info and organize emails into folders. Let me break down what tools they might need. > > First, the agent needs to access the inbox. Gmail API is a must since they're on Gmail. That tool should fetch emails, maybe with filters like sender or date. Then, the agent might need to parse each email's content. So a parser tool that extracts headers, body, attachments, and metadata. > > Next, categorization is key. A categorization tool that uses the LLM to assign tags based on content. It should take the parsed email data and return tags. But how does the LLM know the categories? Maybe the tool needs a list of predefined categories. > > Then, the agent needs to organize emails into folders. A folder management tool that interacts with Gmail's API to move emails. It should take the email ID and target folder. > > Wait, the user mentioned safety. They want to ensure data is safe. So maybe a data sanitization tool to remove sensitive info before processing. That would handle PII or other private data. > > Also, the agent might need to handle attachments. An attachment handler to extract text from PDFs or other files, or maybe scan for malware. But the user said they'll handle the logic, so maybe just the extraction part. > > What about user preferences? A preferences tool to store and retrieve user-defined categories or rules. That way, the agent can adapt to different setups. > > Wait, the user wants the agent to use reasoning. Maybe a reasoning tool that checks if the categorization is accurate based on context. Like, if an email is about a meeting, it should go to the 'Meetings' folder. But how does the LLM know the context? The reasoning tool could use the parsed data and existing categories to make decisions. > > Also, error handling. A validation tool to check if the categorization makes sense. If an email is misclassified, it should flag it for review. > > Wait, the user mentioned tools for the LLM to use. So each tool should be a function the LLM can call. Let me structure them as numbered items with descriptions, input, and output. > > 1. Email Fetcher: Get emails with filters. > 2. Email Parser: Extract headers, body, attachments. > 3. Categorization Tool: Assign tags using LLM. > 4. Folder Manager: Move emails to folders. > 5. Data Sanitizer: Remove sensitive info. > 6. Preference Loader: Retrieve user categories. > 7. Reasoning Tool: Validate categorization. > 8. Attachment Handler: Process attachments. > 9. Validation Tool: Check for errors. > 10. Log Tool: Track actions for auditing. > > Wait, maybe some of these can be combined. For example, the categorization tool could include reasoning. Also, the data sanitizer should be part of the parsing process. Let me make sure each tool is distinct and covers necessary functions. The user wants high-level tools, so the list should be comprehensive but not too granular. I think that's a solid list. Let me present it in numbered format with descriptions.
Here’s a list of tools you should consider including in your LLM agent for email triage, along with their purposes, input/output, and how they contribute to the workflow: --- ### **1. Email Fetcher Tool** **Purpose**: Retrieve emails from the inbox (e.g., Gmail) based on filters (sender, date, keyword, etc.). **Input**: Filters (e.g., `"from:example.com"`, `"label:unread"`, `"after:2023-01-01"`). **Output**: List of raw email data (headers, body, attachments, metadata). **Why**: Enables the agent to access and process emails dynamically based on user-defined criteria. --- ### **2. Email Parser Tool** **Purpose**: Extract structured data from raw email content (headers, body, attachments, metadata). **Input**: Raw email data (e.g., raw JSON from Gmail API). **Output**: Structured data (e.g., sender, subject, body text, attachment filenames, timestamps). **Why**: Converts unstructured email data into a format the LLM can reason about. --- ### **3. Categorization Tool** **Purpose**: Assign tags/categories to emails using the LLM’s reasoning capabilities. **Input**: Parsed email data + predefined category list (e.g., `"Work"`, `"Personal"`, `"Urgent"`). **Output**: Assigned tags/categories (e.g., `["Work", "Urgent"]`). **Why**: Enables the agent to classify emails based on content, sender, subject, or intent. --- ### **4. Folder Management Tool** **Purpose**: Move emails to designated folders (e.g., Gmail labels) based on categorization. **Input**: Email ID + target folder (e.g., `"label:Work"`, `"label:Archive"`). **Output**: Confirmation of email movement. **Why**: Automates the organization of emails into folders, reducing manual effort. --- ### **5. Data Sanitizer Tool** **Purpose**: Remove or anonymize sensitive information (e.g., PII, credit card numbers) from emails. **Input**: Parsed email data. **Output**: Cleaned data (e.g., redacted personal details). **Why**: Ensures privacy and compliance by protecting user data before processing. --- ### **6. Preference Loader Tool** **Purpose**: Load user-defined rules, categories, or preferences (e.g., "Mark emails from 'boss@example.com' as Urgent"). **Input**: Configuration file or database (e.g., JSON with rules). **Output**: Rules/categories for the LLM to reference during categorization. **Why**: Allows the agent to adapt to user-specific workflows and priorities. --- ### **7. Reasoning Tool** **Purpose**: Validate categorization decisions using contextual reasoning (e.g., "This email is about a project deadline, so it should go to 'Work'"). **Input**: Parsed email data + assigned tags. **Output**: Confirmed or adjusted tags (e.g., `["Work", "Urgent"]`). **Why**: Ensures the LLM’s categorization is logically consistent and contextually accurate. --- ### **8. Attachment Handler Tool** **Purpose**: Process attachments (e.g., extract text from PDFs, scan for malware). **Input**: Attachment file (e.g., `file.pdf`, `file.docx`). **Output**: Extracted text or flagged risks (e.g., `"Attachment contains sensitive data"`). **Why**: Handles non-text content and ensures security for attachments. --- ### **9. Validation Tool** **Purpose**: Check for errors in categorization or data processing (e.g., "This email was misclassified"). **Input**: Categorized tags + parsed data. **Output**: Error flags or suggestions for correction. **Why**: Ensures the agent’s decisions are reliable and reduces false positives. --- ### **10. Log Tool** **Purpose**: Track the agent’s actions (e.g., "Moved email X to 'Work' folder"). **Input**: Action details (e.g., email ID, folder, timestamp). **Output**: Audit log for transparency and debugging. **Why**: Provides accountability and helps troubleshoot issues. --- ### **Bonus: User Feedback Tool** **Purpose**: Allow users to correct or refine the agent’s decisions (e.g., "Mark this email as 'Personal'"). **Input**: User input (e.g., corrected tags). **Output**: Updated rules or preferences for future processing. **Why**: Enables iterative improvement of the agent’s categorization logic. --- ### **Key Considerations for Safety** - **Data Isolation**: Ensure all tools operate on sanitized data to avoid exposing sensitive info. - **Access Control**: Restrict tools to only the data they need (e.g., the folder management tool should only handle email IDs, not full content). - **Audit Trails**: Use the log tool to track all actions for compliance and debugging. This structure balances flexibility, security, and automation while leveraging the LLM’s reasoning capabilities. Let me know if you’d like a sample workflow or code sketch!