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

10s

Input Tokens

1350

Output Tokens

1262

Cost

$0.00

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: Analyzing the context and next steps in the flight search task:

1. **Current Status**: We have completed step 2 (search flights) and received 6 available flights from London to Paris for tomorrow (2025-05-29).

2. **Remaining Workflow**: According to the trajectory, we need to:
   - Step 3: Filter by price and select top 3 cheapest options (this is the next step)
   - Step 4: Store top 3 options in memory under key 'top_flights'
   - Final step: Present summary to user

3. **Available Options**: Looking at the flights tool methods:
   - `filter_by_price` - would filter results, but we already have the search results
   - `sort_by_duration` - sorts by duration, not relevant for cheapest
   - `get_cheapest_option` - returns only 1 option, but user wants top 3
   - `get_flight_details` - retrieves detailed info for specific flights

4. **Best Approach**: According to rule 3 in the instructions, "Always store meaningful results in memory using the memory tool. This means after every tool call, you store the results in memory." 

   The most logical next step is to call `flights.get_flight_details` to retrieve detailed information about each of the 6 flights returned, then manually identify the top 3 cheapest options. Once identified, we would store them using `memory.store_item` with the key 'top_flights'.

   This approach is precise because:
   - It follows the tool chain (we have flight IDs/identifiers from the search results)
   - It ensures we have complete pricing information for each flight
   - It sets up for accurate filtering to find the cheapest 3

5. **Why not other options**:
   - `filter_by_price` would typically be called during the search, not after
   - `get_cheapest_option` only returns 1 result, insufficient for user request
   - No direct "get_top_3_cheapest" method exists

Response: flights.get_flight_details