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

11s

Input Tokens

1086

Output Tokens

772

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: Based on the context, I understand that we're in the middle of a multi-step task to find flights from London to Paris for tomorrow. The calendar tool has already been called and returned '2025-05-29' as tomorrow's date. Now, according to the task, I need to 'call the flight tool, update the scratch pad'. This means I should prepare to use the flights tool with the parameters we have: from_city='London', to_city='Paris', and date='2025-05-29'. I'll also update the scratch pad to reflect this action and our progress through the task.

Response: Updating scratch pad: Current step is to call the flights tool with parameters from_city='London', to_city='Paris', and date='2025-05-29'. Progress: Moving to step 3 of 4. Next action will be to compile and present the flight results to the user after the flights tool returns its response.