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

13s

Input Tokens

1155

Output Tokens

676

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: Analyzing the current state of the task execution: The user requested information about flights from London to Paris for tomorrow. The trajectory shows: (1) User request received, (2) Calendar tool called to determine tomorrow's date - returned '2025-05-29', (3) Current cycle is 3 where we need to interpret the calendar result and decide next action. The remaining goals are to use the flights tool to get available flights for 2025-05-29 and then present results. The progress indicates we're at Step 2 of 4. All previous steps have been completed correctly - the calendar tool returned the date in the correct format (YYYY-MM-DD), and the date matches the tool requirements. The next logical action is to call the flights tool with from_city='London', to_city='Paris', and date='2025-05-29'. There are no deviations, errors, or obstacles in the current trajectory. The workflow is progressing as planned.

Response: ON TRACK - The task execution is progressing correctly. The calendar tool successfully returned tomorrow's date (2025-05-29), and the next step is to call the flights tool to search for available flights from London to Paris for that date. All previous steps have been completed successfully with no issues or deviations from the planned trajectory.