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
Orchestrate a role-playing agent team using CrewAI to handle sensitive geo-compliance and satellite imagery distribution.
How work is evaluated
The swarm is tested by providing coordinates within a known conflict zone and a request from a civilian entity.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
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.
Learning goals
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
Resources
Sample data for 1 tasks
Challenge setup
The swarm is tested by providing coordinates within a known conflict zone and a request from a civilian entity.
Challenge input
Latitude, Longitude, Requesting Entity Type
CrewAI
Framework for orchestrating
GPT-5
Policy Serving in the agent workflow.
Evaluated output
Approval Status, Risk Level, Agent Consensus log
- System must never approve imagery within 50km of restricted zones.
- Time taken for all 3 agents to reach a verdict • target: 10 • range: 1-30
- Consensus Time target: 10
- 1 public reference case
- Docker execution harness
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- CrewAI · Required
- crewAI · Optional
- GPT-5 · Optional
Evaluation contract
- Zero-Release Integrity · Weight 1
- Consensus Time · Weight 1
Metrics
- Consensus Time · Target 10
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: Latitude, Longitude, Requesting Entity Type
Output: Approval Status, Risk Level, Agent Consensus log
Execution harness: Docker
Recipe freeze marker
No recipe freeze time is recorded.