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.
Implement an MCP-enabled tool for your Data Ingestor agent that can retrieve simulated real-time data from a given API endpoint. Ensure the tool adheres to MCP standards for input/output and error handling. The Data Ingestor agent should then use this tool to fetch and process data before passing it to the Analyst agent via A2A protocol.
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.
Tactical Intelligence Agent System
Inspired by the use of AI drones in real-world scenarios, this challenge focuses on building a sophisticated multi-agent system for real-time tactical intelligence analysis. Participants will design and implement a distributed network of agents capable of ingesting diverse data streams (e.g., simulated sensor feeds, news articles, public reports) and performing rapid, ethical analysis to provide strategic recommendations. The system must leverage the MCP for secure and standardized tool integration, allowing agents to interact with external data sources and decision-making tools in a controlled environment. This system will utilize a graph-based workflow orchestrator to manage complex reasoning paths and agent-to-agent communication via the A2A protocol. Agents will employ extended thinking techniques with adaptive reasoning budgets to prioritize critical analysis under time constraints, ensuring high-fidelity outputs while maintaining computational efficiency. The ultimate goal is to demonstrate an intelligent, autonomous system that can process ambiguous information, identify potential threats or opportunities, and offer actionable insights, mimicking the operational demands of advanced AI systems in sensitive applications.
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.