Biomedhacked

You can see here the agent is scanning the inventory database, analyzing the telemetry logs for variance.

Inspiration

Biomedical equipment maintenance is currently reactive—hospitals wait for devices to fail before acting. I was inspired to bridge this gap by creating an autonomous agent that shifts the paradigm from "break-fix" to "predict-prevent," ensuring critical medical infrastructure remains reliable.

What it does

It is an autonomous AI agent that monitors real-time clinical telemetry data. It detects calibration drift in life-critical equipment (like ventilators), cross-references the findings with inventory databases, and automatically generates maintenance work orders to prevent device downtime.

How we built it

The agent was built using Python and the Google GenAI SDK. It leverages Gemini 3.5 Flash for reasoning and decision-making, with Pandas for structured data handling. The system uses a modular tool-calling architecture designed for seamless integration with enterprise-grade clinical maintenance platforms.

Challenges we ran into

The primary challenge was managing API stability during high-demand periods. I addressed this by implementing robust error handling and switching to optimized model routes. I also had to balance the agent's autonomous logic with the strict safety constraints required for clinical engineering workflows.

Accomplishments that we're proud of

I successfully developed an end-to-end autonomous agent that can process raw sensor data and convert it into actionable administrative documentation without human intervention. The project demonstrates a clear, scalable path toward proactive hospital operations.

What we learned

I gained deep experience in AI agent orchestration, specifically in managing API rate limits and structuring effective "tool-use" prompts. I also learned how to translate complex clinical requirements into a clean, automated software workflow.

What's next for Biomedhacked

The next steps include integrating real-time streaming data via Google Cloud Pub/Sub, scaling the agent to support a wider array of medical devices, and implementing BigQuery for long-term predictive trend analysis to forecast equipment lifecycle and maintenance needs.

Built With

bigquery
cloudsql
gemini
google-cloud
googleaistudio
pip-install
python
python-run-agent.py

Submitted to

Google Cloud Rapid Agent Hackathon

Created by

Logic & Orchestration: You wrote the Python code that orchestrates the entire agent, including the critical decision-making loop where the agent analyzes telemetry, identifies drift, and executes the necessary tool calls.

Data Handling: You designed the data structure (using Pandas) to simulate a hospital's inventory and maintenance logs, making the agent "testable" in a realistic environment.

Prompt Engineering: You crafted the specialized instructions that allow the Gemini model to behave as a professional biomedical technician, ensuring its "reasoning" is grounded in clinical maintenance standards.

System Stability: You took ownership of the project's technical reliability by implementing error handling, model switching, and optimizing the agent to work within the constraints of the current API environment.

Product Definition: You transformed a broad idea into a specific, functional tool, defining the project's identity (BiomedGuard), the problem it solves (proactive vs. reactive maintenance), and its future roadmap.

Technical Documentation: You documented the stack, identified the necessary Google Cloud integrations, and prepared the project for submission by mapping out the "pitch" and "impact" narrative.

Aditi Tiwari

Updates

Aditi Tiwari started this project — Jun 11, 2026 01:55 PM EDT

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