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Structured source with 49 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using Mastra AI, define a primary `ProductivityOptimizerAgent`. This agent should be capable of:
1. Retrieving documents from a RAG system.
2. Analyzing structured and unstructured data using an LLM (Phi-3).
3. Triggering external automation workflows via Lyzr.
Provide the TypeScript code for the Mastra AI agent initialization, including defining these tool interfaces. Assume Phi-3 is accessible via `phi3.generate(prompt)` and Lyzr tools are accessible via a `lyzrClient.triggerWorkflow(name, params)`.
```typescript
import { createAgent, createTool } from '@mastra-ai/core';
const phi3Tool = createTool('phi3_analyzer', {
description: 'Analyzes data using the Phi-3 LLM.',
input: { type: 'string', name: 'prompt' },
output: { type: 'string' },
handler: async ({ prompt }) => {
// Simulate Phi-3 API call via Oracle OCI Generative AI
return `Analysis result for: ${prompt} (simulated by Phi-3)`;
},
});
const ragTool = createTool('rag_retriever', {
description: 'Retrieves relevant documents from a vector database.',
input: { type: 'string', name: 'query' },
output: { type: 'array', items: { type: 'string' } },
handler: async ({ query }) => {
// Simulate RAG query to Pinecone/Chroma
return [`Doc related to ${query}`];
},
});
const lyzrWorkflowTool = createTool('lyzr_workflow_trigger', {
description: 'Triggers an automation workflow in Lyzr.',
input: {
type: 'object',
properties: {
workflowName: { type: 'string' },
parameters: { type: 'object' },
},
},
output: { type: 'boolean' },
handler: async ({ workflowName, parameters }) => {
// Simulate Lyzr API call
console.log(`Triggering Lyzr workflow: ${workflowName} with params:`, parameters);
return true;
},
});
const ProductivityOptimizerAgent = createAgent({
id: 'productivity_optimizer',
model: 'phi-3',
tools: [phi3Tool, ragTool, lyzrWorkflowTool],
// ... define initial state and goals
});
export { ProductivityOptimizerAgent };
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
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This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
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.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.