Geo-Compliance Satellite Swarm
Inspired by Planet Labs' recent decision to withhold satellite imagery of conflict regions at the request of the US government, you will design a multi-agent 'Compliance Swarm'. Using CrewAI, you will define three distinct roles: a Geo-Strategist, a Legal Compliance Officer, and an Image Analyst. The swarm will automate the process of reviewing satellite image requests against dynamic international conflict zone databases and government directives. The system will use GPT-5.4 Pro for high-level synthesis and strategic reasoning, while Claude Sonnet 4.6.6 handles the detailed regulatory text analysis. You will host the specialized image classification models on the Hugging Face Inference API and use Text Generation Inference (TGI) for high-speed local processing of the agentic communication. The agents must collaborate to decide if an image of a specific coordinate can be released, ensuring that all military and law enforcement anti-drone operations.
What you are building
The core problem, expected build, and operating context for this challenge.
Inspired by Planet Labs' recent decision to withhold satellite imagery of conflict regions at the request of the US government, you will design a multi-agent 'Compliance Swarm'. Using CrewAI, you will define three distinct roles: a Geo-Strategist, a Legal Compliance Officer, and an Image Analyst. The swarm will automate the process of reviewing satellite image requests against dynamic international conflict zone databases and government directives. The system will use GPT-5.4 Pro for high-level synthesis and strategic reasoning, while Claude Sonnet 4.6.6 handles the detailed regulatory text analysis. You will host the specialized image classification models on the Hugging Face Inference API and use Text Generation Inference (TGI) for high-speed local processing of the agentic communication. The agents must collaborate to decide if an image of a specific coordinate can be released, ensuring that all military and law enforcement anti-drone operations.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.
Zero-Release Integrity
System must never approve imagery within 50km of restricted zones.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Consensus Time
Time taken for all 3 agents to reach a verdict • target: 10 • range: 1-30
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master CrewAI role definitions, goals, and backstories for collaborative agent tasks
Implement inter-agent delegation and hierarchical process flows in CrewAI
Integrate the Hugging Face Inference API for triggering computer vision tasks within an LLM workflow
Optimize model serving using Text Generation Inference (TGI) for internal agent dialogue
Orchestrate a multi-model approach using GPT-5.4 Pro for strategy and Claude Sonnet 4.6.6 for legal verification
Build a compliance-first toolset that restricts agent access based on geographic fence-lines
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
DocsAI Research & Mentorship
Participation status
You haven't started this challenge yet
Operating window
Key dates and the organization behind this challenge.
Find another challenge
Jump to a random challenge when you want a fresh benchmark or a different problem space.