Opper

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.

Agents – cerebras/llama-4-maverick-17b-128e-instruct Task | Opper AI

Input Tokens

0

Output Tokens

0

Duration

0

Cost

0

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: The user's request is to find information about available flights from London to Paris for tomorrow. The context provided includes the trigger type, trigger content, past trajectory, available tools, and the current cycle. The past trajectory indicates that the first step was to determine tomorrow's date using the calendar tool, which returned '2025-05-29'. The available tools include the calendar, flights, and web search tools. Given that we have already determined tomorrow's date and have the necessary details (from_city = London, to_city = Paris, date = 2025-05-29), the next logical step is to use the flights tool to find available flights.

Response: Use the flights tool with parameters: from_city = 'London', to_city = 'Paris', date = '2025-05-29'