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

Output Tokens

284

Cost

$0.02

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 responded to the reminder-days question with 'Three days before is fine.' According to the plan in the scratch_pad, step 2 was getting this input. The next remaining goal is to search emails to find the yearly company dinner date. So the next action is to use the email tool's search_emails action with a query related to the yearly company dinner.

Response: The next action is to search the user's emails for information about the yearly company dinner. Specifically, AgentX should call the `email.search_emails` tool with a query like "yearly company dinner" to locate the relevant email. The reminder preference (3 days before) will be stored in working memory for later use when creating the calendar event. After retrieving matching emails, the agent will then read the most relevant one to extract the event date and details before proceeding to check the calendar for conflicts and create the event with a 3-day-prior reminder.