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: 1. The user's task is to determine the next action after flights.search_flights failed twice. Rules state max 3 retries.
2. Rule 2 explicitly allows up to 3 tool call attempts. Two failures mean one remaining retry capacity.
3. No alternative tools exist for flight search in the available toolset (context shows flights tool is only flight-related method).
4. The logical path remains: complete search_flights attempt cycle before considering error escalation.
5. After this retry, either proceed with results or follow failure protocol (log error, inform user if third failure occurs).
6. Instructions explicitly require persisting with tool retries until reaching max limit (rule 2).

Response: Retry flights.search_flights a third time (final allowed attempt per rule). If fails: 1) Store error in memory using memory.store_item with key='flight_search_error', 2) Log timeout pattern, 3) Proceed to inform user about service unavailability.