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 – anthropic/claude-3.7-sonnet Task | Opper AI

Input Tokens

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Output Tokens

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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: Let me carefully analyze the current situation:

1. The user requested information about flights from London to Paris for tomorrow.
2. We're currently on cycle 3 of this task.
3. In previous cycles, we determined that 'tomorrow' is 2025-05-29 by using the calendar tool.
4. We have several tools available: calendar, flights, and web_search.
5. The flights tool requires three parameters: from_city, to_city, and date.
6. We now have all the necessary information to search for flights using the flights tool.

Since we've already determined tomorrow's date (2025-05-29) and we know the departure city (London) and arrival city (Paris), the logical next step is to use the flights tool to retrieve the available flights.

Response: My next action is to use the flights tool to search for available flights from London to Paris for 2025-05-29. I'll execute this by calling the flights tool with the following parameters:
- from_city: London
- to_city: Paris
- date: 2025-05-29

This will provide the user with the flight information they requested.