Operator-ready prompt for reuse, tuning, and workspace runs.
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Structured source with 26 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement the 'Market Researcher' agent using Pydantic AI. This agent should be capable of performing simulated web searches or API calls to gather market trend data. Define a Pydantic model for its output that captures key trends and their potential impact. Integrate a basic 'search' tool (you can simulate this with a function that returns predefined JSON data) and ensure the agent uses Gemini 2.5 Pro to synthesize findings into the structured Pydantic output.
```python
from pydantic import BaseModel, Field
from pydantic_ai import Agent
# Define structured output model for market trends
class MarketTrend(BaseModel):
name: str = Field(..., description="Name of the market trend")
description: str = Field(..., description="Detailed description of the trend")
impact: str = Field(..., description="Impact on SaaS industry")
class MarketResearchReport(BaseModel):
trends: list[MarketTrend]
summary: str
# Implement a simulated search tool
def simulated_search_tool(query: str) -> dict:
# In a real scenario, this would call a search API
if "agentic AI trends" in query:
return {"data": "Recent reports show 30% YoY growth in agentic AI adoption for enterprise SaaS, driven by automation and predictive analytics. Major players are investing heavily."}
return {"data": "No relevant data found for query."}
# Define your Market Researcher agent
class MarketResearcher(Agent):
# ... your agent implementation using Gemini 2.5 Pro to process search results ...
pass
# Example usage:
# researcher = MarketResearcher(model_name="gemini-2.5-pro", tools=[simulated_search_tool])
# report = researcher.run(task="Analyze current agentic AI market trends")
```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
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Agentic SaaS Competitive Intelligence
This challenge focuses on building a sophisticated multi-agent system to provide competitive intelligence and strategic recommendations for a SaaS company facing market pressures from new agentic AI tools. Leveraging the structured output capabilities of Pydantic AI and the advanced reasoning of Gemini 3 Pro, developers will design and implement a team of specialized agents. These agents will autonomously research market trends, analyze competitor offerings (especially new AI-powered solutions), and evaluate internal performance metrics to identify vulnerabilities and opportunities. The system will emphasize data quality and integrity, using Cleanlab for pre-processing and validating research inputs. Agent interactions will be orchestrated to ensure a coherent analysis, culminating in actionable strategic insights. Observability and evaluation are paramount, with Arize AI integrated to monitor agent performance, output quality, and decision-making processes, ensuring the system provides reliable and impactful intelligence for business leaders.
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.