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.
Provide your AutoGen agent team with the feature request: 'Develop a Python function that extracts email addresses from a given text and returns them as a list. Include comprehensive unit tests.' Run the system and collect the generated code, tests, and all Model Context Protocol interaction logs. Conduct an audit of the Model Context Protocol logs to ensure agents only used Model Context Protocol for external calls and followed any defined mock security policies. Submit the code, tests, and the Model Context Protocol audit report.
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 rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.
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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Automate Secure Enterprise Code
This challenge focuses on building a sophisticated multi-agent system for secure, automated software development within an enterprise environment. Participants will architect a team of specialized agents using AutoGen to collaborate on a given software development task, from requirements gathering to code generation, testing, and deployment. The core innovation lies in integrating these agents with enterprise systems (e.g., version control, CI/CD, internal knowledge bases) through the MCP. MCP will ensure secure, audited, and controlled access for agents to sensitive enterprise tools and data. GPT-5 will serve as the primary reasoning engine for complex code generation and architectural decisions, employing extended thinking and adaptive reasoning budgets to tackle intricate problems. This challenge emphasizes production-ready agent deployment, security, and seamless integration into existing enterprise workflows.
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.