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

326

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: The user has provided the required information regarding the reminder days for the company dinner. According to the `scratch_pad` and the `trajectory` in the provided `context`, the next logical step in the plan is to search the user's emails to find the date and details of the yearly company dinner. The `email` tool with the `search_emails` action is the appropriate tool to perform this task. I will use a search query like 'yearly company dinner' to find relevant emails.

Response: {"tool_code": "print(email.search_emails(query='yearly company dinner'))"}