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

11s

Input Tokens

1264

Output Tokens

488

Cost

$0.01

Context

Input

Asses the current state and decide if we are on track or not

Expected output

We are on track

Model output

Reasoning: Looking at the context, the user requested flight information for 'tomorrow'. The agent correctly recognized the need to determine the actual date for 'tomorrow' and used the calendar tool, which returned '2025-05-29'. The scratch pad shows the next step is to use the flights tool with this specific date, which perfectly aligns with the tool's parameter requirements (from_city, to_city, date in YYYY-MM-DD format). Since the agent has successfully completed the prerequisite step and has a clear, actionable plan for the next step, the process is fully on track.

Response: Yes, we are on track. The agent correctly identified that it needed to resolve 'tomorrow' into a specific date before searching for flights. It successfully used the calendar tool to obtain the date '2025-05-29'. The current plan logically outlines the next step: using the 'flights' tool with the gathered parameters (London to Paris on 2025-05-29), which perfectly matches the required schema for the flights tool. The progress is steady and focused directly on fulfilling the user's request.