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

1179

Output Tokens

354

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 task is to assess the current state and decide if we are on track. From the context, we see:

1. Trigger: user request to find flights from London to Paris for tomorrow.
2. Past trajectory shows that the agent has completed two steps: (1) recognized the request and (2) called the calendar tool to determine tomorrow's date, resulting in '2025-05-29'.
3. Tools available include 'flights', 'calendar', and 'web_search' — all functional.
4. Current cycle is 3; scratch pad indicates agent is interpreting the calendar result and planning next action.
5. Remaining goals: use 'flights' tool with correct parameters (from_city: 'London', to_city: 'Paris', date: '2025-05-29'), then present results.
6. Progress marked as 'Step 2 of 4' implies a 4-step plan, likely: (1) understand request, (2) get date, (3) search flights, (4) present results.

Thus, the agent is on track: calendar date successfully retrieved, and next logical step is to execute the 'flights' tool. No errors or deviations noted. All required elements for the flights tool are ready.

Response: Yes, we are on track. The calendar tool has returned tomorrow's date as '2025-05-29', and we are now at Step 2 of 4. Next step is to use the flights tool to search for flights from London to Paris on that date, then compile and present the results.