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

19s

Input Tokens

1086

Output Tokens

288

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 task is to assess if we are on track with finding information about flights from London to Paris for the specified date, using the available tools. The current context shows that we are on a multi-step process where the user request triggered a series of actions. We have already determined the date '2025-05-29' using the calendar tool, and the next step is to find available flights for that date using the 'flights' tool. Our reasoning here is based on assessing the progress of the task and ensuring we are following the steps in the right order. We are currently at step 2 of 4 in the process. Since the calendar tool provided the correct date, our next action should be straightforward: utilize the flights tool with the given parameters (from_city: 'London', to_city: 'Paris', date: '2025-05-29'). This ensures we are directly following the planned trajectory without deviation, and preparing to compile and present results next.

Response: We are on track with the current task. The next step is to use the 'flights' tool with the parameters {'from_city': 'London', 'to_city': 'Paris', 'date': '2025-05-29'} to find available flights, as this is the next logical step in our current cycle and trajectory.