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

15s

Input Tokens

2823

Output Tokens

276

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: 1. Understood the user's request: The user needs to find information about available flights from London to Paris for tomorrow. 2. Interpreted the current state: We have determined tomorrow's date using the calendar tool. 3. Set up the plan of actions: The next step is to use the 'flights' tool to get available flights from London to Paris for the date '2025-05-29'. 4. Strategy: Always aim for the most direct and effective path, remember to iterate over cycles, keep track of progress, use scratch notes, and plan next actions logically. 5. Confirmation: The date format matches tool requirements and the flight search result will be stored in working memory for summary.

Response: The current state is that we are on track to complete the task. The user asked to find information about available flights from London to Paris for tomorrow, and we have determined tomorrow's date to be '2025-05-29'. In the next step, we plan to use the 'flights' tool to get available flights from London to Paris for the date '2025-05-29'.