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

1522

Output Tokens

239

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 requires identifying the next action after two failed attempts to call flights.search_flights due to timeout errors, based on the provided instructions. Key instruction: Max retries for a tool call = 3. Log and explain failures. Since only two failures have occurred, the next action is to attempt the third and final retry of the flights.search_flights tool with the same parameters (from_city='London', to_city='Paris', date='2025-05-29'). If this fails, then consider alternatives like using another tool or reporting to the user, but for now, retry. Follow the cycle: Analyze the failure (timeout, possibly network issue), Plan to retry once more, Act by calling the tool again, Reflect on outcome, Decide next (e.g., if success, store in memory; if fail, escalate). Avoid redundant calls beyond this. Ensure to store results in memory if successful.

Response: Attempt the third and final retry of the flights.search_flights tool call with parameters: from_city='London', to_city='Paris', date='2025-05-29'. Log the previous failures as timeout errors for transparency.