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.

Agents – gcp/gemini-2.5-pro Task | Opper AI

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Context

Input

The user has replied: "Three days before is fine."  According to the plan for the yearly company-dinner task, what is the next action?

Expected output

Call email.search_emails with a query such as "yearly company dinner" (optionally including the current year) to locate the relevant email(s) containing the event details, and store the returned email_id(s) for the subsequent email.read_email step.

Model output

Reasoning: The agent's plan, as outlined in the scratch_pad, was to first get the user's preference for the reminder timing. The user has just provided this information ("Three days before is fine."). The next step in the established plan is to find the date of the event. The plan specifies this should be done by searching the user's emails. Therefore, the next action is to use the `email.search_emails` tool to find the relevant email containing details about the company dinner.

Response: Based on the plan in the scratch_pad, the next action is to search the user's emails for information about the yearly company dinner. The agent should use the `email.search_emails` tool with a query like "yearly company dinner" to find the date and details of the event.