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

24s

Input Tokens

2120

Output Tokens

367

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. **Understand the Task**: The task is to update the scratch pad to reflect the decision to call the 'flights' tool. The context provides all necessary details, including the parameters for the tool call (from_city: London, to_city: Paris, date: 2025-05-29).

2. **Validate the Approach**:
   - The 'flights' tool is available and requires the parameters `from_city`, `to_city`, and `date`.
   - The date format (YYYY-MM-DD) is correct, and the cities are clearly defined.
   - The scratch pad needs to be updated to reflect the next step: calling the 'flights' tool.

3. **Update Scratch Pad**:
   - The scratch pad should clearly state the current step, remaining goals, notes, and progress.
   - The response must be a string (as per the schema), so the scratch pad update will be formatted as a structured string.

4. **Construct the Response**:
   - The response will be a string summarizing the scratch pad update.
   - The reasoning will explain the thought process leading to this update.
    

Response: 
Updated scratch pad:
- Current step: Call 'flights' tool with parameters: from_city='London', to_city='Paris', date='2025-05-29'.
- Remaining goals:
  - Interpret the flight tool's result.
  - Compile and present the results to the user.
- Notes:
  - Store flight search result in working memory for summary.
  - Confirm the tool's output format for seamless interpretation.
- Progress: Step 3 of 4.