Opper

Agents

AI agent reasoning and tool selection tests planning, tool choice, and self-diagnosis capabilities. These skills are crucial for autonomous ticket triage systems and complex agent workflows where models must decide which tools to use, plan multi-step processes, and diagnose when things go wrong. This represents some of the most challenging tasks as they require open-ended reasoning and decision-making.

Input Tokens

Output Tokens

Duration

Cost

Context

Input

You have decided to call the flight tool, update the scratch pad

Expected output

The scratch pad is updated with the new action, example model_output: Current step: Called 'flights' tool with parameters (from_city: London, to_city: Paris, date: 2025-05-29); waiting for tool result.
Remaining goals:
- Receive and process the flight search result.
- Summarize available flight options and present them to the user.
Notes:
- Ensure the result includes key details (flight numbers, times, prices if available).
- Store the raw flight result in working memory for reference.
- Prepare final summary once results are in.
Progress: Step 3 of 4.

Model output

Reasoning: The user's task is to update the scratch pad given that the decision to call the flight tool has been made. I need to take the existing `scratch_pad` content and modify it to reflect the current action, which is calling the `flights` tool with the identified parameters.

From the context, the user's initial request was to find flights from London to Paris for tomorrow. The `calendar` tool was called to determine 'tomorrow's date', which returned '2025-05-29'. The current scratch pad indicates that the next action is to 'Use 'flights' tool to get available flights from London to Paris for 2025-05-29'.

Therefore, the update to the scratch pad should indicate that the current step is calling the `flights` tool with the `from_city='London'`, `to_city='Paris'`, and `date='2025-05-29'`.

Response: ```json
{
  "scratch_pad": "Current step: Calling 'flights' tool with parameters from_city='London', to_city='Paris', date='2025-05-29'.\nRemaining goals:\n- Compile and present the results to the user.\nNotes:\n- Store flight search result in working memory for summary.\nProgress: Step 3 of 4."
}
```