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.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Develop at least two Semantic Kernel native functions that simulate interactions with an enterprise CRM (e.g., retrieve client history, update policy status). Implement the 'Policy Customizer' agent to use these tools via the Semantic Kernel planner to generate a policy and update the client record.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Orchestrate Insurance Policy Automation
There is potential for generative AI to revolutionize complex enterprise workflows. This challenge focuses on building a sophisticated multi-agent system to automate aspects of the insurance policy lifecycle, specifically dynamic risk assessment and personalized policy generation. Your system will use Claude Sonnet 4 for efficient reasoning over vast policy documents and external data. It will leverage Haystack for advanced Retrieval-Augmented Generation (RAG) to ensure accuracy and compliance, and Semantic Kernel for robust tool integration with legacy enterprise systems and structured databases. The goal is to create a hybrid reasoning system that combines the flexibility of LLMs with structured rules and real-time external data, adapting to evolving risk profiles and regulatory changes.
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.