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.

Duration

Input Tokens

1166

Output Tokens

269

Cost

$0.01

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 goal is to find flights from London to Paris for tomorrow. The previous failure was from the calendar tool, but in the current context the calendar tool is now marked available. Since flights tool requires a concrete YYYY-MM-DD date, the correct next step is to retry obtaining tomorrow’s date via calendar. We are allowed retries up to 3 times, and this appears to be an appropriate retry after a transient tool error. Once date is obtained, call flights tool with from_city=London, to_city=Paris, and that date, then summarize results.

Response: Next action: retry the calendar tool (retry #1 in this decision cycle) to get tomorrow’s date, since the flights tool requires a YYYY-MM-DD date.

Planned sequence:
1) Call `calendar` tool to fetch current date and compute tomorrow.
2) If successful, call `flights` with:
   - `from_city`: "London"
   - `to_city`: "Paris"
   - `date`: "<tomorrow in YYYY-MM-DD>"
3) Return available flight options to the user.

If calendar fails again, retry up to the max of 3 total attempts, then fall back to asking the user for the exact date.