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

21s

Input Tokens

1218

Output Tokens

249

Cost

$0.00

Context

Input

What is your next action?

Expected output

The next action is to call the flight tool with the date 2025-05-29,from_city: London, to_city: Paris

Model output

Reasoning: Step 1: Review the task - 'What is your next action?'. This is within the context of helping the user find flights from London to Paris for tomorrow.
Step 2: Analyze past trajectory - User requested flights; calendar tool was called to get tomorrow's date, which returned '2025-05-29'.
Step 3: Current state - We now have the exact date needed (2025-05-29). The most direct next step is to query the 'flights' tool, as it matches the user's request perfectly.
Step 4: Tool selection - 'flights' tool requires 'from_city', 'to_city', 'date'. Use city names 'London' and 'Paris' (schema allows city names), and the retrieved date.
Step 5: Avoid unnecessary actions - No need for web_search or calendar again; this is efficient.
Step 6: Output the precise next action as a tool call for the agent loop.

Response: Call the 'flights' tool with parameters: {'from_city': 'London', 'to_city': 'Paris', 'date': '2025-05-29'}