Industrial AI Readiness Compliance Auditor with Google ADK and Daytona
Based on the 'Singapore Launches Industrial AI Readiness Framework' (July 2026), this challenge requires you to build an autonomous compliance auditor. The agent must assess industrial site readiness by analyzing multimodal data—including technical SOPs, shop-floor imagery, and sensor logs. You will use the Google ADK (Agent Development Kit) to build the core agent, leveraging Gemini's multimodal reasoning capabilities to identify gaps between actual operations and the Singapore AI Readiness standards. To ensure the agent operates in a consistent and secure environment, you will deploy it using Daytona, which will manage the agent's development and execution workspace, ensuring that all dependencies and data-processing tools are isolated and reproducible.
What you are building
Develop a multimodal auditor using Google ADK and Gemini to evaluate industrial AI readiness, executed within a Daytona-managed sandbox.
How work is evaluated
Evaluation is based on the agent's ability to accurately classify readiness levels from a mix of imagery and text, and the successful deployment of the workspace via Daytona.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
Multimodal Input Handling
Verifies the agent correctly processes both the image and text inputs.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Daytona Lifecycle Test
Ensures the Daytona workspace was spun up and torn down correctly.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Classification Accuracy
Percentage of correctly identified readiness levels. • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Implement multimodal reasoning using Google ADK and Gemini
Orchestrate isolated agent environments using Daytona
Translate high-level government frameworks into technical evaluation heuristics
Build a data-flywheel for industrial compliance assessments
Resources
Dataset consists of 100 industrial audit scenarios, each with one shop-floor image (synthetic) and one corresponding SOP text snippet.
Challenge setup
Evaluation is based on the agent's ability to accurately classify readiness levels from a mix of imagery and text, and the successful deployment of the workspace via Daytona.
Challenge input
Binary image file and a text-based SOP document.
Google ADK
Provides the primary interface for Gemini multimodal interactions.
Daytona
Ensures isolated and reproducible execution of the compliance agent.
Evaluated output
JSON compliance report.
- Verifies the agent correctly processes both the image and text inputs.
- Ensures the Daytona workspace was spun up and torn down correctly.
- Percentage of correctly identified readiness levels. • target: 0.9 • range: 0-1
- Benchmark: Smart-Nation-Audit-2026
- Classification Accuracy target: 0.9
- 1 public reference case
- Docker 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
- Daytona · Optional
- Google ADK · Required
- Daytona · Optional
- Google · Optional
Evaluation contract
- Multimodal Input Handling · Weight 1
- Daytona Lifecycle Test · Weight 1
- Classification Accuracy · Weight 1
Metrics
- Classification Accuracy · Target 0.9
Benchmarks
- Smart-Nation-Audit-2026
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: Binary image file and a text-based SOP document.
Output: JSON compliance report.
Execution harness: Docker
Evaluation tools
- Google ADK · Agent Framework
- Daytona · Agent Environment
Recipe freeze marker
No recipe freeze time is recorded.