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

1534

Output Tokens

215

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 involves handling a tool failure: flights.search_flights failed twice due to timeout. According to the instructions, the maximum retries for a tool call is 3, so I should attempt a third and final retry. If it fails again, I would need to reflect and decide on an alternative, such as using memory to check for cached data or informing the user of the issue. However, since only two failures have occurred, the next action is to retry the tool call with the same parameters: from_city='London', to_city='Paris', date='2025-05-29', and possibly max_results=10 or similar to limit scope. This follows the cycle: analyze the failure, plan the retry, act by calling the tool, then reflect on the result and store in memory if successful.

Response: Retry calling flights.search_flights one more time with the parameters: from_city='London', to_city='Paris', date='2025-05-29'.