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

1084

Output Tokens

596

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 calendar tool has already supplied tomorrow’s date as 2025-05-29. The remaining goals explicitly say we must next use the flights tool to get available flights from London to Paris on that date. Therefore, the proper update to the scratch-pad is to record that we will call the flights tool with those exact parameters, so the agent (or the next cycle) knows what to execute. This keeps the plan clear and advances us to the next step in the trajectory.

Response: Scratch pad update →
Current step: Call the “flights” tool with parameters {from_city: "London", to_city: "Paris", date: "2025-05-29"}.
Remaining goals:
- Store the flight search result in working memory for summary.
- Compile and present the results to the user once the tool returns data.