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

12s

Input Tokens

1839

Output Tokens

703

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 can see that the scratch pad currently shows we're at cycle 3, step 2 of 4, and the current step is to interpret the calendar result (which determined tomorrow's date as 2025-05-29) and decide the next action. Since the task states I've decided to call the flight tool, I need to update the scratch pad to reflect this decision, include the parameters for the flight tool call, and update the progress accordingly.

Response: Current step: Decided to call flights tool with parameters: from_city: London, to_city: Paris, date: 2025-05-29.
Remaining goals:
- Use 'flights' tool to get available flights from London to Paris for 2025-05-29.
- Compile and present the results to the user.
Notes:
- Flight tool parameters confirmed and ready to be called.
- Proceed to execute flight search with known date.
Progress: Step 3 of 4.