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

1635

Output Tokens

677

Cost

$0.00

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: 1.  **Analyze the User's Input:** The user has responded with "Three days before is fine." This directly answers the agent's previous question about the reminder preference.
2.  **Review the Current State:** The provided context and scratchpad indicate that the agent was waiting for this specific piece of information from the user.
3.  **Consult the Plan:** The `scratch_pad` clearly outlines the remaining goals. The very next goal after getting the user's input is to "Search emails to find the yearly company dinner date."
4.  **Determine the Next Action:** Now that the user has provided the required input, the agent can proceed with its plan. The next logical and planned step is to use the `email` tool to search for the relevant email containing information about the company dinner.

Response: Based on the plan, the next action is to search the user's emails to find the date and details of the yearly company dinner. The user's response of "Three days before is fine" has provided the necessary information about the reminder, allowing the plan to proceed to the next step.