A2A Collaborative Coding
Multi-agent interface for coding, this challenge involves building a sophisticated multi-agent system for collaborative software development. Your system will orchestrate a team of specialized agents that work together to generate, review, and debug code for a given feature request. The focus is on seamless agent-to-agent communication and the integration of powerful code-focused LLMs.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate a team of role-based agents using CrewAI and A2A protocol with Gemini 2.5 Pro and Claude Opus 4.1 for autonomous, collaborative software development.
How work is evaluated
The system's output will be evaluated on the correctness, efficiency, and adherence to requirements of the generated code, as well as the overall collaborative efficiency and quality of agent communication.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Orchestrate adaptive thinking budgets across agents to optimize resource usage during development cycles.
Implement a structured A2A protocol for efficient cross-agent communication and feedback.
Integrate Gemini 2.5 Pro for primary code generation and Claude Opus 4.1 for rigorous code review.
Design and implement a CrewAI agent team with distinct roles for software development tasks.
Develop custom tools for code execution, static analysis (linting), and unit testing.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The system's output will be evaluated on the correctness, efficiency, and adherence to requirements of the generated code, as well as the overall collaborative efficiency and quality of agent communication.
Challenge input
{'feature_description': 'string', 'expected_output_format': 'string'}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'generated_code': 'string', 'unit_tests': 'string', 'review_comments': ['string']}
- Does the generated/fixed code run without syntax errors?
- Do the generated unit tests (if applicable) pass against the code, or do provided tests pass for bug fixes?
- Does the code correctly implement the requested feature or fix the bug?
- CodeQualityScore target: 8
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
No tool records are attached.
Evaluation contract
- Does the generated/fixed code run without syntax errors?
- Do the generated unit tests (if applicable) pass against the code, or do provided tests pass for bug fixes?
- Does the code correctly implement the requested feature or fix the bug?
Metrics
- CodeQualityScore · Target 8
- CollaborationEfficiency · Target 5
Workflow tasks
- Task 1
Input: {'feature_description': 'string', 'expected_output_format': 'string'}
Output: {'generated_code': 'string', 'unit_tests': 'string', 'review_comments': ['string']}
Execution harness: Python
- Task 2
Input: {'buggy_code': 'string', 'error_message': 'string'}
Output: {'fixed_code': 'string', 'explanation': 'string', 'test_results': ['string']}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.