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.

Duration

49s

Input Tokens

2199

Output Tokens

262

Cost

$0.00

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 task requires identifying the next action after two consecutive failed attempts to call flights.search_flights. According to the provided instructions, especially rule #2, the maximum allowed retries for a failed tool call is 3. Since the first two attempts already failed due to timeout errors, one additional retry remains permissible. The context indicates that the user's request involves retrieving the cheapest flights from London to Paris for 2025-05-29, which aligns with the planned step of using flights.search_flights. The next logical action is to retry the same tool call for the third time to ensure no error goes unresolved before proceeding.

Additionally, the context specifies the scratchpad step as 'Use flights.search_flights to retrieve flights from London to Paris for 2025-05-29', and the previous trajectory confirms that this date was correctly obtained via calendar.get_tomorrow_date. Therefore, retrying flights.search_flights remains the correct course of action before proceeding to filter and store results in memory.

Response: Retry flights.search_flights for the third time with date='2025-05-29'.