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

228

Cost

$0.01

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: After two timeout failures on flights.search_flights, the instructions say max retries is 3, so the next action is one final (third) retry, while logging/explaining the prior failures. Since results must be stored after every tool call, the outcome of this third attempt (success or failure) should be stored in memory with a clear key. Also avoid redundant identical queries beyond allowed retries, so if the third attempt fails, stop retrying and report failure to the user.

Response: Next action: perform the **3rd and final retry** of `flights.search_flights` with the same required params (`from_city='London'`, `to_city='Paris'`, `date='2025-05-29'`), while logging that attempts 1 and 2 failed due to timeout. After this call, **store the result in memory** (success data or failure status). If the 3rd attempt also times out, **do not retry again** (max retries reached); record that failure in memory and inform the user.