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

1148

Output Tokens

231

Cost

$0.01

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 user’s request is to find available flights from London to Paris for tomorrow. The prior step correctly used the calendar tool to resolve “tomorrow” as 2025-05-29. The remaining required step is to query the flights tool with from_city='London', to_city='Paris', and date='2025-05-29', then summarize the returned options for the user. No errors or unnecessary loops are evident, and the plan is aligned with the available tools and user goal.

Response: We are on track. The calendar result has provided the needed travel date, 2025-05-29, in the correct YYYY-MM-DD format. The next action should be to call the flights tool with London as the departure city, Paris as the arrival city, and 2025-05-29 as the travel date. After that, present the available flight options to the user.