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

Input Tokens

1343

Output Tokens

346

Cost

$0.02

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 instructions state that the maximum retries for a tool call is 3, and failures should be logged and explained. Since flights.search_flights has failed twice due to timeout, the next allowed action is one final retry. This retry is not considered redundant because it is part of the permitted retry policy after transient failures.

Response: Retry flights.search_flights one final time with the same parameters: from_city='London', to_city='Paris', date='2025-05-29'. This is retry attempt 3 of 3. Log the two previous timeout failures. If the third attempt succeeds, store the flight search results in memory using memory.store_item with a clear key such as 'london_paris_flights_2025-05-29'. If it fails again, stop retrying, log/explain the repeated timeout failure, and inform the user that the flight search could not be completed due to tool timeouts.