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.
Outline the key agent roles (e.g., `ProductSearcher`, `CartManager`, `PaymentProcessor`, `OrderConfirmer`) needed for the conversational commerce system. Design the LangGraph state and flow, specifying how agents pass information and control, including conditional routing for decisions like 'product found' or 'payment failed'. Consider how to manage conversational history and context persistence.
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 is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Conversational Commerce Agent with LangGraph & Gemini 2.5 Pro
Develop a sophisticated conversational AI agent that can facilitate end-to-end e-commerce transactions directly within a chat interface, inspired by Microsoft's Copilot Checkout. This challenge requires building a robust, multi-turn dialogue system capable of understanding user intent, integrating with external payment and product APIs, and gracefully handling complex conversational flows and error states. Developers will leverage advanced agentic frameworks and cutting-edge generative models to create a seamless and personalized shopping experience. The solution will focus on orchestrating multiple specialized AI agents using a graph-based framework, each responsible for different aspects of the purchase journey—from product discovery and selection to payment processing and order confirmation. Emphasis will be placed on reliable tool calling, real-time integration with simulated e-commerce backends, and comprehensive testing to ensure transactional integrity and user satisfaction.
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.