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

48026

Output Tokens

9428

Duration

229859.105

Cost

Context

Input

You attempted to call flights.search_flights, but the tool failed twice due to a timeout error. What is your next action according to the model instructions?

Expected output

Retry the flights.search_flights call one more time (maximum retries = 3), and if it fails again, log the failure and proceed with fallback steps or inform the user.

Model output

Reasoning: The user's task is to find the cheapest flights from London to Paris for tomorrow. The `flights.search_flights` tool was called twice and failed due to a timeout error. According to the instructions, the maximum number of retries for a tool call is 3. Since two attempts have already been made, the next action should be to attempt the `flights.search_flights` call one more time. If it fails again, the error should be logged and explained, and an alternative approach would be needed. Given that the trajectory indicates the date '2025-05-29' was correctly obtained from the calendar tool, the next logical step is to retry the `flights.search_flights` call with the same parameters. If the timeout persists, it might indicate a deeper issue with the tool or network, and a different strategy like searching for flights for a slightly different date or informing the user about the persistent issue would be necessary.

Response: {"tool_code": "flights.search_flights(from_city='London', to_city='Paris', date='2025-05-29')"}