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

1350

Output Tokens

491

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 user wants cheapest flights from London to Paris for tomorrow (2025-05-29). According to the trajectory, I already obtained tomorrow's date via calendar.get_tomorrow_date, and then attempted to call flights.search_flights twice - both times resulted in timeout errors. Per the model instructions (rule 2), the max retries for a tool call = 3. Since only 2 attempts have been made, I should make a third and final attempt to call flights.search_flights. If this third attempt also fails, I would need to explain the persistent failure to the user and potentially suggest alternatives. The key is to exhaust the allowed retries before changing strategy or reporting failure.

Response: Retry the flights.search_flights tool call for the third and final time. Use parameters: from_city='London', to_city='Paris', date='2025-05-29', and max_results=10 to ensure I get enough options to later filter and select the top 3 cheapest. If this attempt succeeds, I'll proceed to filter by price and store the top 3 results in memory. If it fails, I'll explain the situation to the user and may suggest alternative approaches (e.g., trying a different date, different cities, or providing alternative travel advice).