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

1143

Output Tokens

224

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: The current state shows that we are in cycle 3 of a task to find flights from London to Paris for tomorrow. The user's request has been understood, and we have determined that tomorrow's date is 2025-05-29. The next step is to use the 'flights' tool to get available flights for this date. The progress indicates that we are at Step 2 of 4, which means we are on track. The scratch pad confirms that the date format matches the tool requirements, and the plan is to store the flight search result in working memory for summary. Therefore, we are on track to complete the task.

Response: We are on track. The user's request to find flights from London to Paris for tomorrow has been understood, and the date has been determined as 2025-05-29. The next step is to use the 'flights' tool to get available flights for this date. Progress is at Step 2 of 4, and the plan is to store the flight search result in working memory for summary.