This post is my submission for "DEV Education Track: Build Multi-Agent Systems with ADK" (https://dev.to/deved/build-multi-agent-systems).
🌍 GeoHazard AI — Building a Multi-Agent Geological & Climate Risk System with Google ADK
What I Built
Geological hazards such as landslides are rarely caused by a single factor.
They emerge from interaction between terrain conditions and climate dynamics.
In this project, I built an educational Multi-Agent AI system using Google Agent Development Kit (ADK) that mirrors how real scientists collaborate.
Instead of using one large AI model, the system separates expertise into independent agents:
- A Hazard Agent that evaluates geological instability
- A Climate Agent that analyzes environmental forcing
- A combined GeoHazard reasoning workflow
The goal was to demonstrate how multi-agent architecture improves scientific reasoning, transparency, and modular AI design.
This project serves as a learning example for students, developers, and researchers interested in:
✅ Multi-Agent Systems
✅ Scientific AI workflows
✅ Climate & Earth Science applications
✅ Agent specialization using ADK
Cloud Run Embed
⚠️ Deployment Note
The agents were successfully executed during development using Google ADK.
Later, access to the original cloud execution environment became restricted due to project permission and billing limitations.
(Project Name: Geohazard Agents
Project number: 322609188016
Project ID: geohazard-agents
Dashboard
Cloud Hub
https://console.cloud.google.com/welcome?project=geohazard-agents)
Since this submission focuses on the Educational Track, the project is presented as a reproducible learning system rather than a live deployment.
Readers can deploy the system themselves by:
- Creating a Google Cloud Project
- Enabling Vertex AI
- Installing Google ADK
- Running the agent scripts provided below
This approach emphasizes learning and architecture understanding rather than infrastructure setup.
Your Agents
🪨 Hazard Agent — Geological Specialist
Role:
Evaluates terrain instability and landslide susceptibility.
Responsibilities
- Analyze slope conditions
- Identify terrain risk factors
- Classify hazard level
Design Principle
The agent is intentionally restricted:
«It does NOT analyze climate data.»
This teaches role separation, a core concept in Multi-Agent Systems.
Example tool logic:
@hazard_agent.tool
def landslide_tool(slope: float, rainfall: float):
if slope > 30 and rainfall > 100:
return "High Landslide Risk"
return "Moderate Risk"
🌦️ Climate Agent — Environmental Analyst
Role:
Studies climate influence on hazard amplification.
Responsibilities
- Evaluate rainfall intensity
- Assess temperature anomalies
- Identify environmental stress factors
Design Principle
The climate agent avoids geological interpretation, ensuring:
✅ cleaner reasoning
✅ modular upgrades
✅ independent agent learning
🌐 GeoHazard Reasoning (Multi-Agent Collaboration)
The system demonstrates how agents collaborate:
- Hazard Agent evaluates terrain
- Climate Agent analyzes environmental forcing
- Combined reasoning produces final hazard understanding
This mirrors real interdisciplinary scientific workflows.
🔁 Workflow Overview
Environmental Inputs:
- Slope angle
- Rainfall conditions
- Climate anomalies
Processing Flow:
Environmental Data
↓
Hazard Agent → Geological Risk
↓
Climate Agent → Climate Influence
↓
Integrated GeoHazard Assessment
Example Educational Output:
Hazard Level: High
Climate Risk: Elevated
Integrated GeoHazard Risk: High
Recommended Action: Monitoring and mitigation planning required.
Key Learnings
🧠 1. Multi-Agent AI Thinks Like Scientists
Real experts specialize.
Separating AI agents dramatically improved reasoning clarity compared to a single model handling everything.
⚙️ 2. Constraints Improve Intelligence
Limiting agents to defined responsibilities prevented hallucinated conclusions and produced more structured outputs.
🌍 3. AI for Scientific Discovery Is Practical
Multi-agent systems can assist:
- Landslide early warning
- Climate risk assessment
- Infrastructure planning
- Environmental decision support
🚧 4. Biggest Challenge
The most challenging aspect was understanding how to coordinate agents instead of scaling a single prompt.
This project shifted my perspective from:
«Prompt Engineering → System Engineering»
🎓 Educational Value
This project demonstrates that building intelligent systems is not only about powerful models — it is about designing collaborating AI experts.
Students following this tutorial learn:
- Agent architecture design
- Role specialization
- Scientific reasoning workflows
- Practical use of Google ADK
👨🔬 Author Background
I am a Geologist exploring how Artificial Intelligence can support Earth science research, climate resilience, and geohazard assessment through collaborative AI systems.
🚀 Future Extensions
Possible expansions include:
- Remote sensing agent
- Seismic monitoring agent
- Satellite data integration
- Urban vulnerability analysis
- Climate adaptation advisor
⭐ Thank you for reading.