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

1328

Output Tokens

248

Cost

$0.00

Context

Input

Asses the current state and decide if we are on track or not

Expected output

We are on track

Model output

Reasoning: First, I read the task: Assess the current state and decide if we are on track or not. The context provides the trajectory: User requested flights from London to Paris for tomorrow. Calendar tool was called, returning '2025-05-29'. Current cycle is 3, scratch pad indicates interpreting the tool result and deciding next action. Remaining goals: Use 'flights' tool with the date, then compile and present results. Progress is Step 2 of 4. Instructions emphasize logical planning, using tools directly, and staying focused. Assessing state: The date has been obtained correctly, matching the tool's required format (YYYY-MM-DD). No errors or deviations noted. Next step aligns with goals: Call the 'flights' tool with appropriate parameters. Therefore, we are on track, proceeding efficiently without unnecessary loops.

Response: Yes, we are on track. The calendar tool result has been successfully interpreted, confirming tomorrow's date as 2025-05-29. Next action: Call the 'flights' tool with parameters from_city: 'London', to_city: 'Paris', date: '2025-05-29' to retrieve available flights.