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 wants to find flights from London to Paris for tomorrow. The previous step was to call the calendar tool, which returned the date '2025-05-29'. The next logical step is to use the 'flights' tool to search for flights using the obtained date, along with the specified departure and arrival cities (London and Paris). The scratchpad already indicates this as the next action: 'Use 'flights' tool to get available flights from London to Paris for 2025-05-29.' Therefore, I will update the scratchpad to reflect the decision to call the flights tool.

Response: Updated scratchpad: Current step: Call flights tool to find flights from London to Paris for 2025-05-29. Next action: call flights tool.