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 1 active lines to adapt.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Prepare your Pydantic AI agent application for deployment on `Akash Network`. This will involve containerizing your application using Docker and creating an Akash deployment manifest (YAML). Additionally, integrate `Weights & Biases` into your agent's workflow. Use W&B to log inputs, outputs, LLM calls, and evaluation metrics from your policy extraction and compliance reporting tasks, enabling robust experiment tracking and performance monitoring in a decentralized environment.
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 source structure until you know which part of the prompt is actually driving the result quality.
Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.
Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
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.