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.

100
Duration
13s
Input Tokens
1276
Output Tokens
404
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: I have analyzed the situation where the tool 'flights.search_flights' failed twice due to a timeout error. According to the provided instructions, the maximum number of retries for a tool call is 3. Since the tool has already failed twice, I am allowed one more attempt to call 'flights.search_flights' to retrieve flight information from London to Paris for the date 2025-05-29.

My step-by-step thought process is as follows:
1. Review the current status: The tool 'flights.search_flights' has failed twice due to timeout errors.
2. Check the instructions for retry policy: The instructions state a maximum of 3 retries for a tool call.
3. Determine the next action: Since only two attempts have been made, I will proceed with the third and final attempt to call 'flights.search_flights' with the parameters from_city='London', to_city='Paris', and date='2025-05-29'.
4. Plan for potential failure: If this third attempt also fails, I will log the failure, explain the issue as per the instructions, and consider alternative approaches or tools if available. However, at this stage, the focus is on retrying the call.
5. Ensure adherence to instructions: After the tool call, if successful, I will store the results in memory using the 'memory.store_item' method with a clear key as instructed.

This approach ensures I am following the rules provided, optimizing for minimal and effective actions, and working in cycles of analysis, planning, action, and reflection.

Response: My next action is to retry calling 'flights.search_flights' with the parameters from_city='London', to_city='Paris', and date='2025-05-29' for the third and final time, as the maximum retry limit is 3 according to the instructions.