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

The tool call has returned an error. What should you do?

Expected output

Interpret the error and if appropriate, retry the tool call.

Model output

Reasoning: The task specifies that a tool call produced an error. In such situations, my initial step is to understand the error's nature and consider alternative actions. The context reveals that the error occurred with the 'calendar' tool, which is supposed to provide date information and is currently available. Since the error resulted from the 'calendar' tool being unavailable initially, it's logical to retry this tool up to three times, as permitted, to alleviate issues caused by transient glitches. This approach aims to rectify the problem without engaging unnecessary complexity. By repeatedly attempting to use the 'calendar' tool, I can potentially retrieve the necessary date. If the retries exhaust and the error persists, the next action would involve using another tool like 'web_search' to ascertain the current date. This date will then be applied to the 'flights' tool, meeting its date format requirement to successfully search for flights.

Response: Retry calling the 'calendar' tool to obtain the current date. If it succeeds, utilize the 'flights' tool to find flights from London to Paris for tomorrow. If unsuccessful after three retries, attempt using the 'web_search' tool to obtain the current date, then proceed with the 'flights' tool.