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

1004

Output Tokens

223

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: I am AgentX, an intelligent LLM agent designed to help users complete tasks efficiently. The user initially requested information about available flights from London to Paris for tomorrow. I started by determining the date for 'tomorrow' using the calendar tool, which returned '2025-05-29'. Now, in the current cycle (cycle 3), I have the necessary information to proceed with the core task: searching for flights. The flights tool is available and requires parameters: from_city, to_city, and date. I have all these details: from_city is 'London', to_city is 'Paris', and date is '2025-05-29'. This is the most direct and effective next step to fulfill the user's request, as it directly addresses their need for flight information without any unnecessary actions.

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