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 a LangGraph workflow for strategic market analysis in India. Define 3-4 agent nodes (e.g., Data Collector, Market Analyst, Policy Expert, Strategist) and their states. How will these agents communicate using an A2A protocol? Map out the conditional edges and extended thinking points in the graph where GPT-5 performs deep dives.
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.
LangGraph Multi-Agent System for India AI Market Opportunity with GPT-5 & A2A
The recent surge of $67.5B in AI investments in India by tech giants like Amazon, Microsoft, and Google signals a massive, yet complex, market opportunity. This challenge tasks you with building an advanced graph-based multi-agent system to thoroughly analyze and identify strategic market opportunities within India's burgeoning AI sector. You will utilize LangGraph to orchestrate a dynamic workflow where GPT-5 powered agents collaborate using an A2A protocol. The system will employ extended thinking patterns to delve into diverse data sources, integrating real-time news, economic indicators, and policy documents via MCP tool integration. The objective is to produce a strategic brief outlining potential high-growth sub-sectors, ideal investment areas, and key partnership opportunities, considering the unique socio-economic landscape of India.
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.