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 involves finding flights from London to Paris on 2025-05-29 based on the context that tomorrow’s date is 2025-05-29. According to the plan outlined in the scratch pad, the logical next step is to invoke the flights tool with the correct parameters (from_city=London, to_city=Paris, date=2025-05-29). After calling the tool, we update the scratch pad with the new information (the flights found and any relevant insights). This ensures we are following the user’s instructions and properly recording progress for future steps.

Response: Proceeding with calling the flights tool now. Updating the scratch pad to note we have invoked the flights tool with from=London, to=Paris, date=2025-05-29.