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.
You are the central orchestration agent for a multi-agent project planning system. A new project request has come in: 'Develop an internal knowledge base system for a medium-sized tech company, allowing for document search, categorization, and user-contributed articles. The project needs to be completed within 10 weeks with a team of 4 full-stack developers.' Initiate the hierarchical planning process by defining the initial high-level phases and assigning the first planning tasks to relevant specialist agents, adhering strictly to Model Context Protocol (MCP) principles for communication.
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.
Build an MCP-Enabled Multi-Agent System for Hierarchical Project Planning
This challenge focuses on developing a sophisticated multi-agent system capable of hierarchical project planning. Inspired by advancements in agentic AI and the Model Context Protocol (MCP), participants will design and implement a team of specialized AI agents that can collaboratively break down a complex project goal into manageable sub-tasks, assign them, and refine detailed plans. The system must leverage modern orchestration frameworks like LangGraph to manage agent interactions and state, and integrate a powerful LLM like GLM-4 for advanced reasoning. A core aspect is the implementation of MCP principles to ensure structured, clear, and context-rich communication between agents, facilitating seamless handoffs and preventing misunderstandings in complex planning scenarios. Success will be measured by the system's ability to produce coherent, executable project plans with clearly defined phases, tasks, dependencies, and resource allocations, demonstrating robust multi-agent coordination and hierarchical decomposition.
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.