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

1150

Output Tokens

796

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: Looking at the context, I can see this is about a user request to find flights from London to Paris for tomorrow. The system has already successfully called the calendar tool which returned '2025-05-29' as tomorrow's date. The scratch pad clearly shows we're at step 2 of 4, with the immediate goal being to use the flights tool with the required parameters (from_city: London, to_city: Paris, date: 2025-05-29). The flights tool is available and ready to use with the correct parameters. The remaining goals are clearly defined and the trajectory shows a logical progression: determine date → search flights → present results. There are no errors, blockers, or deviations from the plan.

Response: We are on track. The system is progressing through a logical sequence for the user's flight search request. We have successfully obtained tomorrow's date (2025-05-29) using the calendar tool. The next step, clearly identified in the scratch pad, is to call the flights tool with the required parameters: from_city='London', to_city='Paris', and date='2025-05-29'. All necessary tools are available, the required parameters are ready, and the workflow is following the planned 4-step process. We are currently at step 2 of 4, with no issues or blockers identified.