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

31s

Input Tokens

1191

Output Tokens

761

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: To assess if we are on track, I analyzed the current state against the original goal and planned trajectory. The user requested flights from London to Paris for tomorrow. We have successfully completed step 1: determining tomorrow's date using the calendar tool, which returned '2025-05-29'. This date format (YYYY-MM-DD) matches the flights tool's required parameter format. The scratch pad indicates we are at 'Step 2 of 4' with clear remaining goals: use the flights tool and compile results. The next logical action is directly specified - call the flights tool with the parameters we now have (London, Paris, 2025-05-29). No blockers or errors are present, and we have all required information to proceed. The progression from cycle 1 (user request) → cycle 2 (calendar tool) → cycle 3 (current assessment) follows a logical, efficient path without unnecessary loops.

Response: We are on track. The calendar tool successfully returned tomorrow's date (2025-05-29) in the correct format required by the flights tool. The next action is to immediately call the flights tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'.