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 user’s request triggered a calendar tool call, which returned an error indicating the tool was not available. According to the agent’s instructions, when a tool returns an error, the next step is to think about the error and decide what to do next. The safest approach is: 1) retry the calendar tool up to the maximum allowed retries (3). 2) If the calendar tool still fails after all retries, fall back to an alternative method for obtaining the required date. 3) Use the web_search tool with a query such as "current date" or "what is today's date" to retrieve the date in the correct YYYY-MM-DD format. 4) Once the date is obtained, call the flights tool with "London" → "Paris" and the retrieved date. 5) Collect the flight search results and present them to the user. Therefore, the immediate next action is to retry the calendar tool again, up to three attempts. If it cannot be obtained, switch to web_search to get the date and then proceed with the flights tool.


Response: Your agent should retry the calendar tool up to 3 times. If the calendar tool still fails, use the web_search tool to get today’s date (e.g., "What is today's date?"), then call the flights tool with from_city='London', to_city='Paris', and the obtained date. Finally, present the flight options to the user.