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

18s

Input Tokens

1139

Output Tokens

186

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 has requested that I update the scratchpad. The context provides the current state of the task: finding flights from London to Paris for 2025-05-29. I need to determine the next action based on the progress and remaining goals. The current step is to interpret the calendar tool result. The remaining goals are to use the 'flights' tool and compile results. Therefore, the next action is to call the 'flights' tool with the specified parameters and update the scratchpad.

Response: I will now call the flights tool with the parameters from_city: London, to_city: Paris, and date: 2025-05-29. Then, I will update the scratchpad with the flight search results.