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

165

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: We already attempted flights.search_flights twice and both failed due to timeout. The instructions set a maximum of 3 retries per tool call, so the next action is to perform the third (final allowed) retry. We should also follow the cycle approach: acknowledge/log the previous failures, then act by retrying once more. After the tool call returns (success or failure), we must store the result (or failure details) in memory per the instructions.

Response: Retry `flights.search_flights` one more time (third and final allowed attempt per the max-retries=3 rule), explicitly logging that the first two attempts timed out. After the attempt, store the outcome (results or the timeout failure details) in `memory.store_item`.