Productivity Agent for Enterprise Knowledge Worker
Significant productivity gains from AI tools underscores the demand for intelligent automation in professional tasks. This challenge focuses on building a multi-agent system designed to boost enterprise productivity by automating common workflows. Participants will use CrewAI to orchestrate a team of specialized agents, powered by Claude Opus 4.5 for its superior reasoning and context handling. The system will feature robust MCP-enabled tool integration, allowing agents to interact with simulated or actual enterprise systems like CRM, project management, and internal knowledge bases. Agents will demonstrate adaptive thinking, adjusting their 'thinking budget' based on task complexity and urgency. The goal is to create a 'Productivity Bot' that can intelligently plan and execute multi-step professional tasks, delivering tangible time savings and efficiency improvements within a corporate environment.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a CrewAI-based multi-agent system using Claude Opus 4.5 to automate common professional tasks, integrating with enterprise systems via MCP-enabled tools.
How work is evaluated
The evaluation will assess the CrewAI system's ability to efficiently and accurately automate complex professional tasks, demonstrating effective agent collaboration, tool integration, and adaptive thinking. The focus will be on the quality of the generated output and the efficiency of the workflow.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design agents capable of adaptive thinking and dynamic task adjustment.
Master CrewAI for defining and orchestrating role-based agent teams.
Implement MCP-enabled tool integration with enterprise APIs for automated workflows.
Build a user-facing interface for interacting with the productivity bot.
Develop multi-step planning agents for complex professional tasks.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The evaluation will assess the CrewAI system's ability to efficiently and accurately automate complex professional tasks, demonstrating effective agent collaboration, tool integration, and adaptive thinking. The focus...
Challenge input
{'project_name': 'string', 'jira_data': [{'task_id': 'string', 'status': 'string', 'assignee': 'string', 'comments': 'string'}], 'salesforce_data':...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
A JSON object containing: {'report_title': 'string', 'overall_status': 'string', 'key_updates': 'string', 'upcoming_milestones': 'string', 'risks_i...
- Ensure the output is a valid JSON adhering to the specified schema.
- Check if all critical sections of the report and email are present and populated.
- Information Accuracy Score target: 0.9
- 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.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Ensure the output is a valid JSON adhering to the specified schema.
- Check if all critical sections of the report and email are present and populated.
Metrics
- Information Accuracy Score · Target 0.9
- Report Coherence & Quality · Target 4
- Tool Integration Effectiveness · Target 0.85
- Workflow Efficiency (Total Time) · Target 60
Workflow tasks
- Task 1
Input: {'project_name': 'string', 'jira_data': [{'task_id': 'string', 'status': 'string', 'assignee': 'string', 'comments': 'string'}], 'salesforce_data': [{'client_name': 'string', 'interaction_summary': 'string', 'opportunity_stage': 'string'}], 'stakeholders': ['string']}
Output: A JSON object containing: {'report_title': 'string', 'overall_status': 'string', 'key_updates': 'string', 'upcoming_milestones': 'string', 'risks_issues': 'string', 'summary_email_draft': {'subject': 'string', 'body': 'string'}, 'workflow_log': [{'agent': 'string', 'task': 'string', 'tool_used': 'string', 'duration_seconds': 'int'}]}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.