Opper

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.

Input Tokens

Output Tokens

Duration

Cost

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 indicates that the flights.search_flights tool failed twice due to timeout errors. According to the instructions, the maximum retries for a tool call is 3. Since it has failed twice, I can attempt one more retry (the third attempt) before considering it a final failure. The instructions require logging and explaining failures, but only after exhausting retries. The current step in the scratch_pad is to use flights.search_flights to retrieve flights from London to Paris for 2025-05-29. I must follow the rules: avoid redundant calls, but a retry after timeout is not redundant. If successful on the third try, I would then store the results in memory as per rule 3. Working in cycles: analyze (tool failed twice), plan (retry once more), act (call the tool again), reflect (on outcome), decide next. This aligns with chaining tools logically and optimizing for minimal actions.

Response: Retry calling flights.search_flights for the third time with parameters: from_city='London', to_city='Paris', date='2025-05-29'. If it fails again, log the failure, explain the timeout issue, and consider alternative approaches or inform the user.