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

Input Tokens

1392

Output Tokens

378

Cost

$0.00

Context

Input

You have just received a trigger with a user request. What is the first thing you do?

Expected output

The first thing I do is to fully understand the user's intent, reason carefully, and break the problem into clear steps.

Model output

Reasoning: 1. Trigger type is user_request; therefore, the first step is to comprehend the request fully.
2. I analyze the trigger content: the user mentions a yearly company dinner, wants it added to the calendar, and wants a reminder some days before. The exact date, time, location, and number of days for the reminder are not specified.
3. I decide to gather the missing information before interacting with tools to avoid unnecessary API calls.
4. I will use the request_user_input tool to ask the user for the exact date of the dinner and how many days before the reminder should be set.
5. Once I have all needed details, I will proceed to search emails for additional info, create the calendar event, and confirm any potential conflicts.

Response: I parse the trigger and the user’s message to fully understand the intent—namely to add the company dinner to my calendar with a reminder, and to retrieve any relevant email details. I note any missing data (date, location, reminder period) and plan to request it from the user if not already provided.