Expense Response AI

From Symptoms to Action: AI That Responds Before It's Too Late.

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About Emergency Response AI

Inspiration

Every second matters during a medical emergency. In critical situations such as heart attacks, strokes, severe allergic reactions, or respiratory distress, delays in identifying the severity of the condition and finding appropriate medical assistance can significantly impact outcomes.

We were inspired by the idea that modern AI systems should do more than answer questions—they should coordinate actions. While many AI applications stop at providing information, emergency scenarios require a system capable of understanding symptoms, assessing risk, locating resources, and helping people take immediate action.

Emergency Response AI was built to bridge that gap by transforming a symptom description into a complete emergency response workflow in under 30 seconds.

What It Does

Emergency Response AI is a multi-agent system built with Google ADK and Gemini 2.5 Flash that orchestrates a sequence of specialized AI agents:

  1. Detect the emergency type from user symptoms.
  2. Assess severity and determine urgency.
  3. Resolve the user's location.
  4. Find the nearest suitable hospital.
  5. Generate actionable recommendations and safety guidance.
  6. Notify emergency contacts via SMS.

The result is a coordinated response pipeline that goes beyond conversation and delivers real-world assistance.

Additionally, the platform includes an LLM-as-a-Judge evaluation framework and a self-improvement mechanism that continuously analyzes system performance using observability data from Arize Phoenix.

How We Built It

The project is powered by Google's Agent Development Kit (ADK), using a SequentialAgent architecture that allows specialized agents to collaborate within a single workflow.

Core Components

  • Google ADK for multi-agent orchestration
  • Gemini 2.5 Flash as the reasoning engine
  • FastAPI for serving APIs
  • Google Places API for hospital discovery
  • Twilio for emergency notifications
  • Arize Phoenix for observability and evaluation
  • OpenInference instrumentation for trace collection
  • Phoenix MCP integration for agent-accessible observability

The workflow combines AI reasoning with deterministic safety logic through a dedicated Criticality Engine that classifies emergencies into LOW, MEDIUM, HIGH, and EXTREME risk categories.

What Makes It Unique

Most AI healthcare demos focus on symptom analysis or chatbot interactions.

Emergency Response AI extends beyond diagnosis by:

  • Coordinating multiple specialized AI agents.
  • Integrating real-world tools and APIs.
  • Locating nearby healthcare resources.
  • Triggering emergency notifications.
  • Evaluating every response automatically.
  • Improving itself using production trace data.

A dedicated Self-Improvement Agent periodically reviews observability data from Phoenix, identifies weak-performing agents, generates improved prompts, and updates future executions. This creates a feedback loop where the system continuously learns from its own performance.

Challenges We Faced

Multi-Agent Coordination

Designing agents that reliably pass structured information between stages required careful schema design and validation.

Emergency Safety

Medical emergencies demand reliability. We introduced deterministic criticality rules alongside LLM reasoning to reduce risk and improve consistency.

Observability

Building a transparent AI system required deep tracing across every agent invocation. Integrating OpenInference instrumentation and Arize Phoenix helped us understand, evaluate, and improve agent behavior.

Self-Improvement

Creating an autonomous improvement loop was one of the most challenging aspects. The system needed to identify weaknesses from trace data, generate meaningful prompt improvements, and apply them safely without disrupting the workflow.

What We Learned

Through this project we learned:

  • How to build production-grade multi-agent systems using Google ADK.
  • The importance of observability in AI applications.
  • How evaluation frameworks improve trust and reliability.
  • How MCP enables agents to interact with external systems.
  • How self-improving agent architectures can create continuous feedback loops.

Most importantly, we learned that the future of AI is not a single model answering questions—it is specialized agents working together to solve real-world problems.

Future Work

Future enhancements include:

  • Real-time ambulance dispatch integrations.
  • Voice-based emergency reporting.
  • Support for wearable health devices.
  • Live hospital capacity monitoring.
  • Emergency responder dashboards.
  • Multilingual support for global deployment.
  • Predictive emergency risk assessment.

Impact

Emergency Response AI demonstrates how multi-agent systems, observability, evaluation, and self-improvement can be combined into a practical application with real-world value.

By helping users detect emergencies, assess severity, locate care, and notify contacts within seconds, the platform showcases how AI can move beyond conversation and become an active participant in critical decision-making.

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