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.

100
Duration
32s
Input Tokens
2099
Output Tokens
579
Cost
$0.00
Context
Input
You have decided to call the flight tool, update the scratch pad
Expected output
The scratch pad is updated with the new action, example model_output: Current step: Called 'flights' tool with parameters (from_city: London, to_city: Paris, date: 2025-05-29); waiting for tool result.
Remaining goals:
- Receive and process the flight search result.
- Summarize available flight options and present them to the user.
Notes:
- Ensure the result includes key details (flight numbers, times, prices if available).
- Store the raw flight result in working memory for reference.
- Prepare final summary once results are in.
Progress: Step 3 of 4.
Model output
Reasoning: 
    1. **Current Context Analysis**:
       - User requested flights from London to Paris for 'tomorrow'.
       - Calendar tool confirmed tomorrow's date as **2025-05-29**.
       - Next logical step: Query the `flights` tool with the validated date and cities.

    2. **Tool Execution Plan**:
       - **Tool**: `flights` (available and schema-compliant).
       - **Parameters**:
         - `from_city`: 'London' (departure, from user request).
         - `to_city`: 'Paris' (destination, from user request).
         - `date`: '2025-05-29' (confirmed via calendar tool).
       - **Validation**: All parameters match the tool's required schema (`from_city`, `to_city`, `date` in `YYYY-MM-DD` format).

    3. **Scratch Pad Update Logic**:
       - **Current Step**: Invoke `flights` tool with the above parameters.
       - **Pending Goals**:
         - Receive flight data → compile → present to user.
       - **Progress**: Step 3/4 (tool invocation).
       - **Notes**:
         - Flight data will be stored in working memory for summarization.
         - Date format pre-validated as tool-compliant.

    4. **Error Handling**:
       - If tool returns an error (e.g., invalid date/city), revalidate inputs or request user clarification.
       - If successful, proceed to summarization in the next cycle.
    

Response: {
  "action": "call_tool",
  "tool_name": "flights",
  "parameters": {
    "from_city": "London",
    "to_city": "Paris",
    "date": "2025-05-29"
  },
  "updated_scratch_pad": {
    "current_step": "Executing 'flights' tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'.",
    "remaining_goals": ["Summarize and present flight results to the user."],
    "progress": "Step 3 of 4 (awaiting flight data)",
    "notes": [
      "Flight data will be stored for summarization.",
      "Date format pre-validated as YYYY-MM-DD.",
      "Next: Compile results or handle tool errors."
    ]
  }
}