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: As an agent, I understand the task given is to update the scratch pad by using the 'flights' tool to find available flights from London to Paris for the date obtained from the calendar tool, which is '2025-05-29'. To achieve this, I will follow these steps: 1. Confirm that the date format matches the 'flights' tool requirements. 2. Use the 'flights' tool with the specified parameters: from_city='London', to_city='Paris', and date='2025-05-29'. 3. Store the flight search result in working memory for summary. 4. Compile and present the results to the user. The reason for this approach is to follow the given guidelines and efficiently complete the task.

Response: Available flights from London to Paris for 2025-05-29 will be found using the 'flights' tool. Results will be presented to the user after completion.