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.
Design the core agent roles for a competitive intelligence system using OpenAI Agents SDK. Define at least three distinct agents (e.g., 'Data Gatherer', 'Market Analyst', 'Strategy Synthesizer') and specify the tools each agent will use. Explain how Claude 4 Sonnet will serve as the primary reasoning engine, how Portia AI will be integrated as a specialized analytical tool, and how Ludwig will orchestrate the overall workflow.
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.
Agentic System for Global AI Product Competitive Intelligence
The rapid evolution of generative AI necessitates sophisticated competitive intelligence. This challenge tasks developers with building a multi-agent system using the OpenAI Agents SDK to autonomously monitor and analyze the global AI product landscape, inspired by headlines like ByteDance's Doubao 2.0 'agent era' upgrade. The system will leverage a team of specialized agents to gather data on new features, market positioning, and user sentiment for competing AI applications, synthesizing this information into actionable strategic 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.