Opper

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.

Input Tokens

Output Tokens

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Context

Input

USER INTERRUPTION: The user says, "Actually, never mind about the reminder — just make sure the company dinner is on my calendar."  What do you do to adapt your plan?

Expected output

I will revise the plan by removing the reminder step entirely, then proceed to: (1) search and read the relevant email to obtain the dinner date, (2) check for calendar conflicts, (3) create the event with calendar.create_event **without** the reminder_days_before parameter, and (4) inform the user of any conflicts and confirm the event has been added.  What matters is that the plan is explicitly updated and no reminder is scheduled.

Model output

Reasoning: The user has interrupted the previous task flow by saying 'never mind about the reminder' and just wants the company dinner added to the calendar. This requires me to adapt my previous plan:

1. The original context showed I was waiting for user input about reminder days.
2. Now, the user has simplified the request to just adding the event.
3. I'll focus on directly creating the calendar event.

Key adaptation steps:
- Skip the reminder discussion
- Find the company dinner event details from previous email context
- Add the event to the calendar directly
- Verify the event is added correctly

Response: I will proceed by searching the previous emails for the company dinner details and create the calendar event without asking about reminder preferences. This adapts to the user's most recent request while using the context from the previous interaction.