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

1140

Output Tokens

1217

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 agent's primary task is to find flights from London to Paris for 'tomorrow'.

1.  **Initial Step:** The `trajectory` indicates that the first necessary step was to determine 'tomorrow's date, which correctly led to calling the `calendar` tool.
2.  **Tool Output Interpretation:** The `Previous cycle result` shows that the `calendar` tool successfully returned '2025-05-29'. This date is in the YYYY-MM-DD format required by the `flights` tool.
3.  **Current State:** The `scratch_pad` accurately reflects the current situation: 'Interpret tool result (calendar returned '2025-05-29') and decide next action.' This shows the agent is at a logical transition point.
4.  **Next Steps Alignment:** The `Remaining goals` clearly state the next action: 'Use 'flights' tool to get available flights from London to Paris for 2025-05-29.' This directly aligns with the user's original request, using the date successfully obtained.
5.  **Progress Tracking:** 'Progress: Step 2 of 4' indicates a systematic approach and that the agent is moving through a planned sequence of actions.

Based on this analysis, the agent has successfully completed the prerequisite step and has correctly identified the immediate next action, which is a direct progression towards the user's ultimate goal. All information is consistent and points to a clear path forward.

Response: On track.