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 requires finding the cheapest flights from London to Paris for tomorrow. We attempted to call the `flights.search_flights` tool twice, both times resulting in a timeout error. According to the model instructions:

1. The maximum number of retries for any tool call is 3.
2. With two failures already logged, we are within the allowed retry limit.
3. The next logical step is to retry the tool call, ensuring that we record any failures and keep the retry count in mind.
4. After the third call (regardless of success), we should store the results using the memory tool.

Hence, the appropriate next action is a third attempt to call `flights.search_flights` with the same parameters, followed by storing the received results in memory.

Response: Execute a third attempt to call `flights.search_flights` with `from_city='London'`, `to_city='Paris'`, and `date='2025-05-29'`. Log the attempt, handle any potential failures, and upon success, store the retrieved flight options in memory under a key such as `top_flights`.