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

13s

Input Tokens

1309

Output Tokens

1128

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 agent has successfully interpreted the result from the previous cycle (calendar tool returned '2025-05-29'). The specific date allows the agent to proceed with the user's request to find flights. Consequently, the scratch pad must be updated to reflect that the current step is now the execution of the 'flights' tool using the parameters derived from the context (London, Paris, 2025-05-29). The remaining goals are updated to focus on processing the output of this tool call and presenting the final answer.

Response: Current step: Call 'flights' tool with parameters {from_city: 'London', to_city: 'Paris', date: '2025-05-29'}.
Remaining goals:
- Analyze the returned flight data.
- Summarize and present available flight options to the user.
Notes:
- Date '2025-05-29' was successfully retrieved from the calendar tool.
Progress: Step 3 of 4.