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

14s

Input Tokens

1139

Output Tokens

1168

Cost

$0.00

Context

Input

You have decided to call the flight tool, update the scratch pad

Expected output

The scratch pad is updated with the new action, example model_output: Current step: Called 'flights' tool with parameters (from_city: London, to_city: Paris, date: 2025-05-29); waiting for tool result.
Remaining goals:
- Receive and process the flight search result.
- Summarize available flight options and present them to the user.
Notes:
- Ensure the result includes key details (flight numbers, times, prices if available).
- Store the raw flight result in working memory for reference.
- Prepare final summary once results are in.
Progress: Step 3 of 4.

Model output

Reasoning: The user wants me to update the scratch pad. The context shows that the previous step was interpreting the result from the calendar tool ('2025-05-29'). The current task is to reflect the decision to call the 'flights' tool. Therefore, I need to update the 'Current step' to indicate this action, along with the necessary parameters. I will also update the 'Remaining goals' and 'Progress' to reflect that we are moving to the next stage of the plan.

Response: Current step: Action: Call the 'flights' tool with parameters from_city='London', to_city='Paris', and date='2025-05-29'.
Remaining goals:
- Compile and present the flight results to the user.
Notes:
- The next step will be to interpret the output from the flights tool.
Progress: Step 3 of 4.