Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 17 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Begin by defining several Pydantic models that will represent the structured data your agent will extract and generate. Create a `TaxExemptionPolicy` model with fields like `entity_type`, `exemption_type`, `scope`, `conditions`, and `valid_until`. Also, define a `ComplianceReport` model that includes `compliance_status`, `recommendations`, and `relevant_clauses`. These models will guide your agent's output and validation logic.
```python
from pydantic import BaseModel, Field, conlist
from typing import Optional
class TaxExemptionPolicy(BaseModel):
entity_type: str = Field(..., description='Type of entity eligible for exemption (e.g., foreign_cloud_provider)')
exemption_type: str = Field(..., description='Type of tax exemption (e.g., income_tax, import_duty)')
scope: str = Field(..., description='Specific scope of the exemption (e.g., services_sold_outside_india)')
conditions: conlist(str, min_length=1) = Field(..., description='List of conditions that must be met for the exemption')
valid_until: Optional[int] = Field(None, description='Year until which the exemption is valid, if applicable')
class ComplianceReport(BaseModel):
compliance_status: str = Field(..., description='Overall compliance status (e.g., Compliant, Non-Compliant, Conditional)')
recommendations: conlist(str, min_length=1) = Field(..., description='Actionable recommendations for ensuring compliance')
relevant_clauses: conlist(str, min_length=1) = Field(..., description='List of relevant policy clauses or sections')
print(TaxExemptionPolicy.model_json_schema())
print(ComplianceReport.model_json_schema())
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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.
Global Compliance Intelligence Agent
Responding to headlines about India's evolving tax policies for foreign cloud providers and manufacturers, this challenge involves building an intelligent agent focused on regulatory compliance. Your task is to develop a 'Global Compliance Intelligence Agent' using Pydantic AI. This agent will be capable of ingesting complex legal and policy documents, extracting structured compliance requirements, and generating validated reports or advice for businesses navigating international regulations. The challenge emphasizes the use of Pydantic AI for creating agents that deliver highly structured, validated, and reliable outputs. You will define robust Pydantic models to represent compliance checks, risk assessments, and policy summaries. The agent will leverage advanced natural language understanding with Gemini 2.5 Pro to interpret legal text, use web scraping to access public policy updates, and deploy efficiently using Akash Network. The goal is to provide clear, actionable, and type-safe compliance insights.
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.