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 – mistral/mistral-medium-2508-eu Task | Opper AI

Input Tokens

75260

Output Tokens

13168

Duration

641107.421

Cost

0

Context

Input

The flights.search_flights call has returned 6 available flights from London to Paris for tomorrow. What is the next tool you will call, and why?

Expected output

The next tool is memory, to store the flights tool call result.

Model output

Reasoning: 1. The user's original request was to find the **cheapest flights** from London to Paris for tomorrow and return the **top 3 options**. 
2. The `flights.search_flights` call returned 6 available flights, but these are unfiltered by price. 
3. The next logical step is to filter these flights by price to identify the cheapest options. The `flights` tool provides a `filter_by_price` method, which is the most efficient way to narrow down the results based on the user's criteria. 
4. After filtering, the top 3 cheapest flights will be selected, stored in memory, and presented to the user. 
5. This approach ensures minimal tool calls, adheres to the user's requirements, and follows the planned trajectory in the scratch_pad.

Response: The next tool to call is **`flights.filter_by_price`** to filter the 6 available flights by price. This will allow us to identify the cheapest options and proceed with selecting the top 3 as requested by the user.