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.

Input Tokens

Output Tokens

Duration

Cost

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: Let's analyze the situation step by step:

1. Current Status:
   - We have successfully received 6 flights from the search_flights call
   - The user wants the top 3 CHEAPEST options
   - Looking at the flights tool methods, I see 'filter_by_price' is available
   - Per rule #3, we need to store meaningful results in memory

2. Tool Selection Consideration:
   - We need to get the cheapest flights
   - The flights tool has a 'filter_by_price' method which is exactly what we need
   - This aligns with our planned next step in the scratch pad: "Filter by price and select top 3 cheapest options"

3. Why This Is The Best Next Action:
   - It directly addresses the user's requirement for cheapest flights
   - It follows the logical workflow of first searching, then filtering
   - It's consistent with our planned trajectory

Response: The next tool to call will be flights.filter_by_price. This is the most logical next step because:
1. It will sort the found flights by price, allowing us to identify the cheapest options
2. It directly aligns with the user's request for the 'cheapest flights'
3. It follows our planned sequence in the scratch pad to 'Filter by price and select top 3 cheapest options'
4. After this, we'll be able to store the top 3 results in memory as planned