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

18s

Input Tokens

1086

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: The current task is to assess if we are on track with the user's request about finding available flights from London to Paris for tomorrow. From the context, the user's intent is clear: they want flight information for travel from London to Paris on 2025-05-29, which was correctly derived using the calendar tool. The trajectory shows that we have completed step 2 of 4, which involved getting the date from the calendar tool. The next logical step is to use the 'flights' tool with parameters from_city='London', to_city='Paris', and date='2025-05-29' to obtain the available flights. This aligns perfectly with the remaining goals and steps outlined in the context. We are following the instructions by progressing step-by-step, utilizing tools effectively, and keeping track of the cycle. No deviations or blockers are present. Thus, we are on track and ready to proceed with invoking the flights tool as planned.

Response: We are currently on track with the user request. The date for the flight search has been correctly obtained as 2025-05-29, and the next step is to use the 'flights' tool to find available flights from London to Paris for that date. All processes and plans are aligned with the goals and instructions.