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: I need to analyze the task which requires calling the flight tool and updating the scratch pad. Based on the context, I have all necessary parameters: the user wants flights from London to Paris for tomorrow, and I've already confirmed the date is 2025-05-29 from the calendar tool. The flights tool requires three parameters: from_city, to_city, and date. I'll call this tool with the established parameters and then update the scratch pad to reflect this action and the next step.

Response: Called flights tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'. Updated scratch pad to: 'Current step: Flight tool called to search for available flights. Next action: Interpret flight results and prepare for user presentation. Progress: Step 3 of 4.'