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

613

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 instructions specify a maximum of 3 retries for any tool call. Since the tool has timed out twice already, I have one retry left. The instruction also requires me to log failures and to store meaningful results in memory. Therefore, the correct next action is to perform a third attempt with the same parameters, log the outcome, store it in memory, and then move on.

Response: I will retry calling `flights.search_flights` one more time. According to the rules, I have already made two failed attempts due to timeouts, so this is my third and final retry. I will use the same parameters: `from_city='London'`, `to_city='Paris'`, `date='2025-05-29'`, and `max_results=3`. After the call, I will store whatever result (success or failure) in memory under the key `flight_search_attempt_3` and then proceed to the next step.