Inspiration We wanted to simplify the management of expenses, contracts, tasks, and HR data for executives and entrepreneurs. The goal was to create an AI-powered assistant that adapts to changing data without manual updates.
What it does The agent automatically discovers the database schema (get_db_schema), executes queries to retrieve summary data and reports, and supports secure approval workflows for updates and deletes. It provides concise analytics in the form of a guide.
How we built it We combined dynamic schema discovery with MongoDB queries and aggregation. The agent logic ensures secure operations, approval steps for write operations, and understandable report formats.
Challenges we faced Handling invalid aggregation syntax, preventing tool reinitialization, and ensuring secure delete/update flows required careful design.
Proud Achievements We've created a system that automatically adapts to new collections, generates professional reports, and ensures director approval before any data changes. It demonstrates true scalability for municipal datasets.
What We've Learned Dynamic schema discovery significantly reduces maintenance costs. Clear approval processes prevent accidental data loss. Management-style reviews make technical data accessible to decision-makers.
What's Next for the AI Secretary We plan to expand integration with other systems: Improve natural language queries so directors can instantly obtain useful information for decision-making. Role Expansion: Introduce multiple user roles so the agent can be used not only by directors but also by accountants and lawyers. Instant Document Digitization: Ensure that photographed documents are immediately converted into digital records before being added to the database. Business Recommendations: Provide actionable suggestions from the agent to improve the efficiency and overall productivity of municipal enterprises.
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