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.
Initialize an OpenAI Agent system with two primary agents: 'ArbitrageTrader' and 'RiskManager'. Define the tools for price fetching and battery status updates. Use the OpenAI Agents SDK pattern for tool definitions.
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.
Multi-Agent BESS Merchant Exposure & Arbitrage Optimizer with OpenAI Agents SDK
In response to the increasing acceptance of merchant exposure in the UK and European energy markets, this challenge requires building an autonomous multi-agent system to manage Battery Energy Storage Systems (BESS). Using the OpenAI Agents SDK, you will develop a 'Trading Agent' that optimizes arbitrage across Day-Ahead and Intraday markets and a 'Risk Management Agent' that monitors battery health and volatility. This system must handle the complexities of merchant risk—where revenues are no longer guaranteed by subsidies but driven by market spreads. To ensure the reliability of these autonomous agents in high-stakes energy trading, you will integrate Test.ai to automate the testing of agentic behaviors under extreme market scenarios (e.g., negative pricing or unexpected outages). The final solution will demonstrate a robust framework for maximizing revenue while adhering to physical battery constraints and degradation limits, mimicking real-world operations by firms like ContourGlobal or Iberdrola.
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.