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

Input Tokens

1116

Output Tokens

209

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 task is to call the flight tool and update the scratch pad. From the context, it's clear that the user needs information about available flights from London to Paris for tomorrow, which has been determined to be '2025-05-29'. The next logical step is to use the 'flights' tool to retrieve this information. The parameters required for the flight tool are 'from_city', 'to_city', and 'date', all of which are already available from the context. The scratch pad should be updated to reflect the call to the flight tool and to note that the next step is to compile and present the results to the user.

Response: Called the flight tool with the parameters: from_city='London', to_city='Paris', date='2025-05-29'. Updated the scratch pad to indicate that flight information is being retrieved and the next step is to compile and present the results to the user.