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.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement the core LangGraph workflow for processing an incoming text snippet. This should include agents dynamically calling Model Context Protocol-enabled tools, performing extended thinking with GPT-5, and coordinating with Claude Opus 4.1 for adversarial analysis. Provide code snippets for key nodes and edges.
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.
Build a Factual Integrity Agent System
The proliferation of disinformation, particularly the 'LLM grooming' tactics necessitates robust AI systems for factual verification. This challenge tasks you with building an advanced multi-agent system designed to detect, analyze, and counter sophisticated disinformation campaigns. Your system will leverage cutting-edge large language models and a graph-based agent framework to establish a factual integrity pipeline. Agents will employ extended thinking and adaptive reasoning budgets to critically evaluate information, cross-reference with multiple authoritative sources via MCP-enabled tools, and communicate securely to synthesize verified insights. This project emphasizes developing resilient and context-aware agents capable of distinguishing nuanced truth from elaborate falsehoods in real-time, preparing them for the challenges of an information-saturated digital landscape.
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.