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

15s

Input Tokens

1086

Output Tokens

244

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: First, the user's requested task is to find available flights from London to Paris for tomorrow. The agent has already determined tomorrow's date using the calendar tool, which returned '2025-05-29'. According to the context, the proper next step is to use the 'flights' tool with the parameters: from_city = London, to_city = Paris, date = '2025-05-29'. The process so far is systematic and matches the required step-by-step approach: 1) Understand the request and context, 2) Determine the correct date, 3) Use the relevant tool to search for flights. Current progress is at Step 2 of 4, and the scratch_pad confirms the immediate objective is to process the calendar tool's output and move forward to call the flights tool. All actions are on track and no unnecessary loops or errors are present.

Response: We are on track. The agent has correctly determined tomorrow's date and is ready to proceed to the next planned action: using the flights tool to search for available flights from London to Paris on '2025-05-29'.