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

12s

Input Tokens

1263

Output Tokens

382

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: Let me analyze the current state systematically:

1. User Request Analysis:
   - User wants flight information from London to Paris for tomorrow
   - This is a clear, actionable request requiring flight search

2. Progress Assessment:
   - Currently on cycle 3
   - Calendar tool has been successfully used to get tomorrow's date (2025-05-29)
   - Next planned action is to use the flights tool
   - According to scratch_pad, we are on step 2 of 4

3. Tools Status:
   - All necessary tools are available (calendar, flights, web_search)
   - Calendar tool has been used successfully
   - Flights tool is ready for the next step

4. Trajectory Evaluation:
   - The path taken so far is logical and efficient
   - First got the date (completed)
   - Next step is to search flights (pending)
   - Final step will be presenting results (pending)

Response: Yes, we are on track. The execution is proceeding efficiently and logically. Here's why:
1. The initial step of getting tomorrow's date is complete
2. The next action (using the flights tool) is clearly identified
3. The tools needed are all available
4. The progress (step 2 of 4) indicates we're moving at an appropriate pace
5. The scratch_pad shows clear remaining goals and next steps

The agent can proceed with using the flights tool using the obtained date (2025-05-29) to search for London-Paris flights.