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-flash Task | Opper AI

Input Tokens

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Output Tokens

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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 user's response, "Three days before is fine," addresses the previous action of asking for the reminder preference. According to the `trajectory` and `scratch_pad` in the provided `context`, the agent's next step is to search the user's emails for information about the yearly company dinner to find the event date. This aligns with the first item in the 'Remaining goals' list: 'Search emails to find the yearly company dinner date.' Therefore, the next action is to use the `email` tool to search for relevant emails.

Response: The next action is to search the user's emails for information about the yearly company dinner to find the event date.