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

1117

Output Tokens

562

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 user request is to find flight information from London to Paris for tomorrow. The calendar tool successfully provided the correct date (2025-05-29) and we've moved through two steps as per scratch_pad: getting calendar date confirmation and setting up next actions. The next steps are well-defined (use flights tool with confirmed date and compile results). The tools required (calendar, flights) are available with no errors noted. Current cycle is 3, scratch_pad indicates progress through step 2 of 4. The trajectory is logical and unimpeded – tools were used appropriately, date confirmed matches requirements for flights tool. All systems operational, progress follows plan.

Response: We are on track. The required calendar date has been acquired with correct formatting (2025*/05-29), flight search parameters are ready for execution (London ➔ Paris, 2025-05-29), tools are operational, and trajectory is progressing as planned (step 2/4). Next step: Run flights tool with confirmed parameters.