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 request is to find available flights from London to Paris for tomorrow. In the previous cycle, the calendar tool was used to determine tomorrow's date, which was returned as '2025-05-29'. This date is in the correct YYYY-MM-DD format required by the flights tool. The next logical step is to use the 'flights' tool with the departure city 'London', arrival city 'Paris', and the date '2025-05-29'. After retrieving the flight options, the results will be compiled and presented to the user. I am now updating the scratch pad to reflect that the flight search is being initiated.

Response: I am using the flights tool to search for available flights from London to Paris on 2025-05-29.