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 requested to call the flight tool to find available flights from London to Paris for tomorrow. From the previous cycle, we have the date for tomorrow as '2025-05-29'. The flights tool requires the parameters: from_city, to_city, and date in YYYY-MM-DD format, which we have. The next logical step is to call the flights tool with from_city='London', to_city='Paris', and date='2025-05-29'. After getting the flight tool results, we should update the scratch pad with the results to keep track of progress and compile them for the final user response.

Response: Call the flights tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'. Then update the scratch pad with the flight search results once returned.