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

1155

Output Tokens

709

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: 1) The user requested flights from London to Paris for tomorrow. 2) We successfully called the calendar tool and obtained the date '2025-05-29'. 3) We now have all required parameters to call the flights tool: from_city='London', to_city='Paris', date='2025-05-29'. 4) We are at Step 2 of 4, which aligns with the expected workflow (Step 1: Get date, Step 2: Search flights, Step 3: Process results, Step 4: Present to user). 5) The remaining goals are clearly defined and achievable with the available tools. The trajectory shows logical progression without errors or blockers. The flights tool is available and has all required parameters satisfied.

Response: We are ON TRACK. The previous action successfully retrieved tomorrow's date (2025-05-29) from the calendar tool. We now have all necessary information to proceed with the flight search. Next action should be to call the 'flights' tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'. This will advance us to Step 3 of 4, keeping us on schedule to complete the user's request efficiently.