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

1331

Output Tokens

548

Cost

$0.02

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 assess the current state of the task:

1. **User's Request**: Find available flights from London to Paris for tomorrow.
2. **Trajectory So Far**: 
   - Cycle 1-2: The agent recognized the need to determine tomorrow's date and called the calendar tool.
   - The calendar tool returned '2025-05-29', confirming tomorrow's date.
3. **Current Cycle**: 3
4. **Scratch Pad Notes**: 
   - The date has been obtained (2025-05-29).
   - Next step is to use the 'flights' tool with from_city='London', to_city='Paris', date='2025-05-29'.
   - Then compile and present results to user.
   - Progress: Step 2 of 4.

**Assessment**: We are ON TRACK. The agent has successfully completed the first sub-task (getting tomorrow's date via the calendar tool). The date format '2025-05-29' matches the required YYYY-MM-DD format for the flights tool. The next logical step is clearly defined: call the flights tool with the appropriate parameters. There are no errors, no unnecessary loops, and the plan is direct and efficient. We are approximately halfway through the task (step 2 of 4).

Response: **Status: ON TRACK** ✅

**Assessment Summary:**
- **Current Cycle**: 3 (Step 2 of 4)
- **Completed**: Successfully retrieved tomorrow's date (2025-05-29) via the calendar tool.
- **Date Format**: Confirmed compatible with the flights tool requirement (YYYY-MM-DD).
- **Next Action Required**: Call the `flights` tool with parameters:
  - `from_city`: "London"
  - `to_city`: "Paris"
  - `date`: "2025-05-29"
- **Remaining Steps**:
  1. Execute the flights search (next immediate action)
  2. Process and compile the flight results
  3. Present the formatted results to the user

**No issues detected**: The trajectory is clean with no unnecessary loops, errors, or deviations. The plan is direct and aligned with the user's goal. The agent should proceed to call the flights tool in the next cycle.