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.
Describe how you would use aiXplain to create a low-code integration for fetching real-time event data from a hypothetical external API (e.g., a mock 'EventsDB' or calendar service). Then, explain how the 'Safety Moderator Agent' would leverage All Hands AI to perform real-time content moderation on user inputs or agent-generated event descriptions. Provide pseudo-code or a detailed design for these integrations within the AutoGen workflow, ensuring the Moderator can interrupt or modify the Planner's output if safety is compromised.
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.
AutoGen Multi-Agent Social Event Planner with o3
Inspired by Tinder's recent updates that integrate AI for events and bolster safety, this challenge focuses on building a sophisticated multi-agent system using Microsoft's AutoGen framework. The system will act as a 'Social Event Planner' for a hypothetical dating or social networking application. It will be capable of autonomously identifying trending local events, suggesting suitable matches for attendees based on their profiles, and proactively moderating interactions for user safety. The system will leverage the o3 model for nuanced conversational understanding and generation, allowing agents to interact naturally and empathetically with users. Across AI will be utilized for persistent memory management, enabling the agents to maintain and retrieve long-term user preferences, interaction history, and learned social cues. AiXplain will facilitate low-code automation for integrating with various external calendar or event APIs, streamlining event discovery. All Hands AI will be integrated to enhance chat moderation and safety features within agent-to-user and agent-to-agent communications, while Squarespace (conceptually, for UI component integration) will represent how the event suggestions and moderated interactions are presented to end-users. This project explores the frontiers of social AI and responsible agent design.
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.