Multimodal Flood Resilience Agent with Google ADK and Llama 3.3 70B
Inspired by the recent devastating floods in West Virginia and the failure of unaddressed prevention plans, this challenge tasks developers with building a sophisticated Flood Risk Assessment Agent. Using the Google Agent Development Kit (ADK), you will create an agent that can process multimodal inputs: geospatial satellite imagery of property footprints and historical flood prevention documents. The agent must evaluate the 'resilience gap' between proposed prevention plans and current physical infrastructure. You will utilize Llama 3.3 70B (running on Vertex AI) to parse and summarize complex legislative and engineering reports, while the ADK manages the agentic loop and tool integration for fetching real-time weather alerts and elevation data. The core of the challenge is the implementation of a reasoning chain that can identify if a property's current mitigation measures (visualized via computer vision) align with the state-level prevention strategies. This involves high-stakes decision-making for insurers to adjust underwriting premiums based on 'active resilience' rather than just static FEMA zones.
What you are building
Develop an automated flood risk scoring system that combines multimodal satellite analysis with Llama 3.3 70B text analysis using the Google ADK.
How work is evaluated
Evaluation focuses on the accuracy of the resilience score and the agent's ability to cross-reference text with visual data.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
Logic Consistency
Check if evidence mentions specific clauses from input text
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Precision at 0.5
Accuracy of detecting non-compliant infrastructure • target: 0.85 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Orchestrate multimodal AI agents using Google ADK and Gemini models
Integrate Llama 3.3 70B for large-scale document parsing within a Vertex AI ecosystem
Implement geospatial risk scoring using raster and vector data overlays
Design an explainable insurance underwriting decision support system
Resources
Synthetic dataset containing high-resolution property images with ground-level vs. elevated HVAC systems, paired with semi-structured PDF reports of state flood prevention plans.
Challenge setup
Evaluation focuses on the accuracy of the resilience score and the agent's ability to cross-reference text with visual data.
Challenge input
JSON containing image_url and pdf_text
Google ADK
Used for managing agentic state and multimodal tool use.
Llama 3.3 70B
Provides deep understanding of legislative and engineering text.
Vertex AI
Host for model endpoints and compute.
Evaluated output
JSON with 'score' (0-1), 'violations' (list), and 'evidence' (string)
- Check if evidence mentions specific clauses from input text
- Accuracy of detecting non-compliant infrastructure • target: 0.85 • range: 0-1
- Benchmark: InsuranceML-Resilience-v1
- Precision At 0.5 target: 0.85
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- Modal · Optional
- Google · Required
- Llama 3.3 70B · Optional
- Modal · Optional
Evaluation contract
- Logic Consistency · Weight 1
- Precision at 0.5 · Weight 1
Metrics
- Precision at 0.5 · Target 0.85
Benchmarks
- InsuranceML-Resilience-v1
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing image_url and pdf_text
Output: JSON with 'score' (0-1), 'violations' (list), and 'evidence' (string)
Execution harness: Python
Evaluation tools
- Google ADK · Orchestration Framework
- Llama 3.3 70B · Text Analysis Model
- Vertex AI · Platform
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