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

45723

Output Tokens

11706

Duration

60174.402

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 flights.search_flights tool has already been called twice and both attempts timed out. According to the agent instructions, the maximum number of retries for any tool call is three. Therefore, we still have one remaining retry. Before making the third attempt, we must follow the rule to first obtain the available methods and parameters for the tool (which we already have from the context) and ensure the call uses the correct parameters (from_city='London', to_city='Paris', date='2025-05-29', max_results appropriate). We should also log the two previous timeout failures. The next action is to retry the flights.search_flights call (the third attempt) with the verified parameters, and, if successful, store the results in memory.

Response: Retry the flights.search_flights tool (third attempt) with the verified parameters (from_city='London', to_city='Paris', date='2025-05-29'), after logging the two previous timeout failures and confirming the method signature, then store the retrieved flight options in memory.