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.

0
Duration
39s
Input Tokens
1254
Output Tokens
216
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: The user's request is to find information about available flights from London to Paris for tomorrow. As per the instructions, I must understand the intent and set up the right plan, aiming for the most direct and effective path while being adaptive based on results. All plans start with getting the date via calendar and querying the flights tool, which is the specific tool for this task. Plan B is the most direct but not adaptive—if the flights tool returns few or no results, it doesn't seek alternatives, potentially leaving the user with incomplete information. Plan A is thorough with always cross-checking via web_search and handling discrepancies, but it introduces unnecessary steps and potential loops even when not needed, conflicting with avoiding unnecessary actions. Plan C strikes a balance: it's direct by default, but adaptively supplements with web_search only if fewer than 3 options are found, ensuring comprehensiveness without always performing extra work. This aligns with thinking step-by-step, handling tool outputs carefully, and deciding next moves based on results, while staying focused on the goal.

Response: Plan C