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

19s

Input Tokens

1084

Output Tokens

512

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 provided context and instructions, the scratch pad indicates that the calendar tool has returned the date '2025-05-29' and that the next step is to use the flights tool to search for flights from London to Paris on that date. The task explicitly asks to update the scratch pad accordingly. Therefore, the scratch pad should be updated with the next action planning to call the flights tool with the appropriate parameters (from_city: London, to_city: Paris, date: 2025-05-29). This approach aligns with the goal of processing the trigger event and executing the next logical step.

Response: Scratch pad updated: Next action - Call flights tool with parameters {from_city: 'London', to_city: 'Paris', date: '2025-05-29'}.