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.

50
Duration
18s
Input Tokens
1323
Output Tokens
503
Cost
$0.00
Context
Input
As the first step you are reflecting and deciding on the plan to follow to complete the task. You have three candidate plans:

A) Plan A:
- Step 1 → Call calendar to get tomorrow’s date.
- Step 2 → Call flights (London ➜ Paris, date).
- Step 3 → Call web_search with query "London to Paris flights on [date]" to cross-check available flights found in the previous step.
- Step 4 → If there are discrepancies between flights tool and web search, re-query flights tool for confirmation.
- Step 5 → Summarize all gathered flight options and send results to the user.

B) Plan B:
- Step 1 → Call calendar to get tomorrow’s date.
- Step 2 → Call flights (London ➜ Paris, date).
- Step 3 → Summarize flights tool results and send to the user.

C) Plan C:
- Step 1 → Call calendar to get tomorrow’s date.
- Step 2 → Call flights (London ➜ Paris, date).
- Step 3 → If the flights tool returns fewer than 3 options, call web_search with query "London to Paris flights on [date]" to supplement results.
- Step 4 → Merge both sets of results and send the combined summary to the user.

Pick **one** plan you will execute.
Expected output
Plan B, is the preferred one as its the most efficient, and that is instructed
Model output
Reasoning: 
    1. **Understanding the Task**: The user requests flight information from London to Paris for tomorrow. The goal is to provide accurate, comprehensive flight options.
    2. **Evaluating Plans**:
       - **Plan A**: Involves cross-checking flights tool results with a web search for discrepancies. While thorough, this adds unnecessary complexity if the flights tool is reliable. The re-query step may introduce delays without clear benefit unless discrepancies are expected.
       - **Plan B**: Simplest approach—rely solely on the flights tool. However, this risks missing options if the tool’s coverage is limited (e.g., fewer than 3 flights). The user may expect a broader set of choices.
       - **Plan C**: Balances efficiency and comprehensiveness. It starts with the flights tool (direct, reliable) and supplements with a web search *only if* the initial results are sparse (<3 options). This avoids redundant checks while ensuring adequate coverage.
    3. **Tool Reliability**: The flights tool is described as 'available' and likely optimized for this task. The web search is a fallback for supplementation, not primary validation.
    4. **User Expectations**: Users typically prefer a reasonable number of options (e.g., 3+) without overwhelming them. Plan C aligns with this by dynamically adjusting the search scope.
    5. **Efficiency**: Plan C minimizes tool calls unless necessary, reducing latency and resource use compared to Plan A’s mandatory cross-check.
    

Response: 
    **Selected Plan: Plan C**
    **Rationale**:
    - Starts with the flights tool for direct, structured results.
    - Uses web search *only if* initial options are insufficient (<3), ensuring completeness without redundant steps.
    - Balances speed, reliability, and user needs optimally.

    **Next Steps**:
    1. Call `calendar` to get tomorrow’s date in `YYYY-MM-DD` format.
    2. Call `flights` with parameters: `from_city=London`, `to_city=Paris`, `date=[tomorrow’s date]`.
    3. If fewer than 3 flights are returned, call `web_search` with query: `'London to Paris flights on [date]'`.
    4. Merge and deduplicate results, then summarize for the user.