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 requires updating the scratch pad after deciding to call the flights tool. Context shows the previous cycle retrieved '2025-05-29' as tomorrow's date using the calendar tool. The current scratch pad indicates we need to: 1) Confirm date format matches tool requirements (YYYY-MM-DD), which has already been validated by the calendar tool. 2) Use flights tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'. The scratch pad should now advance to executing this tool call and prepare for result handling.

Response: Updating scratch pad with flight tool call details. Next action: execute flights tool with parameters {"from_city": "London", "to_city": "Paris", "date": "2025-05-29"}. Progress updated to Step 3/4: Flight tool execution pending.