AI-Powered Productivity Agent for Enterprise Cost Optimization
Design and implement a Mastra AI agent system to address the challenge of boosting enterprise productivity and optimizing costs. This system will leverage RAG with internal company data and external industry reports to identify inefficiencies, suggest process improvements, and automate routine analytical tasks. The core challenge is to build a scalable and intelligent agent that can ingest diverse data, perform complex analysis, and recommend actionable strategies, working in concert with other automation platforms like Lyzr.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a Mastra AI agent for enterprise productivity and cost optimization, utilizing RAG with Phi-3 and integrating with Lyzr for workflow automation.
How work is evaluated
The evaluation will focus on the agent's ability to accurately retrieve relevant information, generate insightful analysis, and effectively trigger automation workflows through Lyzr, leading to quantifiable productivity improvements or cost savings.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
ActionableRecommendations
Checks if the agent's recommendations are specific and directly address identified inefficiencies.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
CorrectWorkflowTrigger
Verifies that the agent correctly triggers a Lyzr workflow when appropriate.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
RecommendationAccuracy
Percentage of recommendations that are logically sound and supported by data. • target: 85 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
DataUtilizationScore
A score (1-5) on how effectively the agent used all provided simulated data for analysis. • target: 4 • range: 1-5
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 Mastra AI for building stateful, memory-aware agents and defining their tool-use capabilities and workflows.
Implement a RAG pipeline using a vector database (e.g., Pinecone) to index and retrieve relevant internal company documents and industry reports for Phi-3.
Design and build custom tools for the Mastra agent to interact with enterprise data sources and Lyzr automation workflows (e.g., fetching HR data, expense reports, operational metrics).
Utilize Phi-3 (served via Oracle OCI Generative AI) for advanced data analysis, report generation, and recommending cost-saving initiatives.
Orchestrate a multi-agent workflow where specialized Mastra agents collaborate on different aspects of productivity analysis (e.g., 'Data Analyst Agent', 'Process Improvement Agent').
Develop an evaluation harness to measure the accuracy and impact of the agent's productivity recommendations and automated actions.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will focus on the agent's ability to accurately retrieve relevant information, generate insightful analysis, and effectively trigger automation workflows through Lyzr, leading to quantifiable productivi...
Challenge input
{ "query": "string", "simulated_data_context": { "expense_reports": "array", "operational_metrics": "object", "industry_benchmarks": "object" } }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{ "report_summary": "string", "identified_inefficiencies": "array", "recommended_actions": "array" }
- Checks if the agent's recommendations are specific and directly address identified inefficiencies.
- Verifies that the agent correctly triggers a Lyzr workflow when appropriate.
- Percentage of recommendations that are logically sound and supported by data. • target: 85 • range: 0-100
- RecommendationAccuracy target: 85
- 2 public reference cases
- TypeScript 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
- ActionableRecommendations · Weight 1
- CorrectWorkflowTrigger · Weight 1
- RecommendationAccuracy · Weight 1
- DataUtilizationScore · Weight 1
Metrics
- RecommendationAccuracy · Target 85
- DataUtilizationScore · Target 4
Reference cases
- 2 public reference cases
Workflow tasks
- Task 1
Input: { "query": "string", "simulated_data_context": { "expense_reports": "array", "operational_metrics": "object", "industry_benchmarks": "object" } }
Output: { "report_summary": "string", "identified_inefficiencies": "array", "recommended_actions": "array" }
Execution harness: TypeScript
- Task 2
Input: { "scenario_description": "string", "agent_analysis_output": "object" }
Output: { "lyzr_workflow_triggered": "boolean", "workflow_name": "string", "workflow_parameters": "object" }
Execution harness: TypeScript
Recipe freeze marker
No recipe freeze time is recorded.