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

1148

Output Tokens

277

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: We have correctly interpreted the user’s intent (find available flights from London to Paris for tomorrow) and have already completed the prerequisite step of determining “tomorrow’s” date via the calendar tool, which returned 2025-05-29. The next required step in the plan is to query the flights tool using from_city=London, to_city=Paris, date=2025-05-29 (YYYY-MM-DD matches the tool schema). No flight search has been executed yet, so we are not finished, but we are progressing logically and are on track at cycle 3 with clear remaining goals and an obvious next action.

Response: Status: On track.

Current state:
- User intent identified: find flights London → Paris for tomorrow.
- Prerequisite completed: determined travel date as 2025-05-29 (valid YYYY-MM-DD).

Why on track:
- The workflow is proceeding in the correct order and we have the key missing parameter (date).

Next action needed to continue:
- Call the `flights` tool with: from_city="London", to_city="Paris", date="2025-05-29".

Not yet complete:
- Flight results have not been retrieved or presented to the user yet.