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

1131

Output Tokens

229

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: <think>I have decided to call the flight tool to find available flights from London to Paris for tomorrow, which is 2025-05-29. The context tells us that in the previous cycle, the date 2025-05-29 was obtained by calling the calendar tool. The goal is to use the flight tool with the parameters: from_city='London', to_city='Paris', and date='2025-05-29'.</think>

Response: Calling flight tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'. Updating the scratch pad: Current step: Call 'flights' tool with parameters for London to Paris on 2025-05-29. Remaining goals: Compile and present the flight results to the user. Notes: Store flight search result in working memory for summary. Progress: Step 3 of 4.