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

Input Tokens

1343

Output Tokens

687

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: We have just received six flight options for London to Paris on the target date. The user's goal is to return the three cheapest flights. According to the agent's workflow and the provided instructions, the next logical step is to narrow down these results by price. The Flights tool offers a 'filter_by_price' method that can filter and rank flights based on their cost. Before invoking it, we ensure we know its required parameters (the list of flights and an optional max_results). This complies with the rule to always verify a tool's available methods and parameters before use. Therefore, the next tool call will be flights.filter_by_price with the current flight list and a request for the top 3 cheapest options. This filtered result will then be stored in memory as per the memory‑storage rule.

Response: The next tool I will call is **flights.filter_by_price** with the six retrieved flight records and `max_results = 3`. This will filter the flights by price and give us the three cheapest options, which aligns with the user's request and the planned next step.