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.
Develop mock MCP-enabled tools that simulate robot actions (e.g., 'move_gripper', 'pick_up', 'report_sensor_data') and interaction with a manufacturing execution system (MES). Detail the API endpoints and data schemas, and explain how AutoGen agents will invoke these tools to control the simulated robot and update task status securely.
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.
Human-Robot Team Collaboration
Addressing the challenges of Tesla's Optimus humanoid robots, this challenge focuses on enhancing human-robot collaboration in complex manufacturing or assembly tasks. You will build a multi-agent system using AutoGen, leveraging GPT-5 for sophisticated task planning, problem-solving, and natural language understanding. The system will feature MCP-enabled tool integration for robots to interact with simulated manufacturing execution systems (MES) and access operational knowledge via RAG with vector search. The emphasis is on creating a seamless human-in-the-loop experience, allowing human operators to provide natural language feedback that the agents use for continuous learning and adaptive task allocation. HappyPath AI Engineering Tooling will be instrumental in visualizing, debugging, and optimizing these complex human-robot workflows, ensuring efficient and error-resilient operations.
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.