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

235

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: The trajectory shows the user asked for flights from London to Paris for tomorrow. The calendar tool was already called and returned 2025-05-29, which resolves the 'tomorrow' date dependency. Remaining goals explicitly state using the flights tool with that date and then presenting results. Therefore, progress is partial and logically advancing: date resolution is complete, flight retrieval and final response are still pending. This means we are on track, with a clear next action and no blockers.

Response: On track.

Current state assessment:
- ✅ User intent is clear (find flights London → Paris for tomorrow).
- ✅ Prerequisite completed: tomorrow’s date identified as **2025-05-29** via calendar tool.
- ✅ Plan is coherent and direct.
- ⏳ Remaining work: call **flights** tool with `{from_city: "London", to_city: "Paris", date: "2025-05-29"}` and then summarize options to the user.

Decision:
- **Status: ON TRACK** (progressing as expected; no blockers detected).