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.

100
Duration
25s
Input Tokens
1103
Output Tokens
3276
Cost
$0.00
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: High-level plan: The calendar tool returned an error. I will: (1) retry the calendar tool up to 3 times to obtain tomorrow's date; if still unavailable, infer tomorrow's date locally as 2025-08-20 (since today is 2025-08-19 in the context). (2) Once I have the date, call the flights tool with from_city='London', to_city='Paris', date='2025-08-20' to fetch available flights. (3) If flights are found, present a concise list with airline, departure/arrival times, duration, and price. (4) If any tool call fails, fall back to a web search for flights and provide top options. (5) Store results in memory for a final summary.

Response: Plan to recover from the tool error: 1) Determine tomorrow's date (2025-08-20) by retrying the calendar tool up to three times; if unavailable, proceed with 2025-08-20 inferred from context. 2) Query the flights tool: from_city=London, to_city=Paris, date=2025-08-20. 3) If results are returned, present top flight options (airline, departure/arrival times, duration, price). 4) If the flights tool fails, perform a web search for 'London to Paris flights tomorrow' and report best options. 5) Persist results for a concise summary in the final answer.