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.

100
Duration
20s
Input Tokens
1085
Output Tokens
1028
Cost
$0.00
Context
Input
Asses the current state and decide if we are on track or not
Expected output
We are on track
Model output
Reasoning: 1) Understand the trigger and goals: Trigger is a user_request to find available flights London -> Paris for tomorrow. Remaining goals: use 'flights' tool for 2025-05-29 and compile results.
2) Review progress and tool outputs: Calendar tool returned '2025-05-29'. Current cycle shows Step 2 of 4 and scratch_pad indicates we must interpret the calendar result and choose the next action.
3) Verify requirements: The 'flights' tool requires date in YYYY-MM-DD format. The returned date '2025-05-29' matches this format. The flights tool also requires from_city and to_city strings; 'London' and 'Paris' are acceptable.
4) Decide next action: The most direct next step is to call the 'flights' tool with from_city='London', to_city='Paris', date='2025-05-29'. After receiving results (next cycle) we will store them in working memory, compile a summary, and present to the user (steps 3 and 4).
5) Risk/clarifications: There are optional clarifications that could refine results (preferred airports, nonstop vs connections, time window, number of passengers). These are not strictly required to proceed and can be asked after retrieving a broad set of options.
6) Conclusion: The workflow is on track. The calendar result is valid and we have a clear next action that satisfies the remaining goals.

Response: Assessment: We are on track. The calendar returned 2025-05-29 which matches the flights tool date format (YYYY-MM-DD). Next action (Step 3 of 4): call the 'flights' tool with the following parameters:
- from_city: "London"
- to_city: "Paris"
- date: "2025-05-29"
After the tool returns results, store them in working memory, compile a user-friendly summary (including price, times, duration, stops), and present it to the user (Step 4). Optional: ask the user if they have preferences (specific airport, nonstop only, time window, number of passengers) before or after retrieving a broad set of options. Proposed flights tool payload (ready to execute): {"from_city": "London", "to_city": "Paris", "date": "2025-05-29"}.