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
19s
Input Tokens
1259
Output Tokens
479
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: I compared the three candidate plans against the user’s goal (quickly obtaining reliable flight options for tomorrow).

• Plan A gives a full cross-check with web search no matter what, which ensures accuracy but adds an extra external lookup even when the flights tool alone may be sufficient. That is more work and latency than necessary.

• Plan B is extremely fast but risks providing an incomplete list if the flights tool returns only a few options.

• Plan C starts lean (date → flights) and only invokes an additional web search when the flights tool produces fewer than three options, giving us a balanced approach: efficient in the common case, yet still capable of enriching results when data might be sparse. This matches the instruction to “aim for the most direct and effective path” while avoiding unnecessary calls.

Therefore, Plan C is the most efficient and adaptive choice.

Response: I will execute Plan C.