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

1340

Output Tokens

273

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: Step 1: Review the task - Assess the current state and determine if we are on track. Step 2: Analyze context - The user's request is for flights from London to Paris tomorrow. In cycle 1 (implied), we used the calendar tool to get tomorrow's date as '2025-05-29'. Now in cycle 3, we are at the stage of interpreting this result and planning the next action. Remaining goals include using the 'flights' tool with the confirmed date and then presenting results. The date format is YYYY-MM-DD, which matches the tool's requirements. No issues or deviations noted in progress (Step 2 of 4). Step 3: Decision - We are on track, as the logical next step is to call the 'flights' tool with the parameters: from_city='London', to_city='Paris', date='2025-05-29'. This advances us directly toward the goal without unnecessary steps.

Response: We are on track. The calendar tool successfully returned the date '2025-05-29' for tomorrow. Next action: Call the 'flights' tool with from_city='London', to_city='Paris', date='2025-05-29' to retrieve available flights.