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: Given the task, my first step is to understand the current context. The user has requested information about flights from London to Paris for a specific date, which has been identified as '2025-05-29' by the calendar tool. The next logical step in the process is to use the 'flights' tool to search for available flights for this date. This involves preparing the necessary parameters for the tool (from_city, to_city, date) based on the user's request and the context provided. Additionally, updating the scratch pad to reflect that the flight search is the next action helps in maintaining a clear progression towards the user's goal.

Response: Updating the scratch pad to indicate the next step: calling the 'flights' tool with parameters from_city: 'London', to_city: 'Paris', date: '2025-05-29' to find available flights.