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

16s

Input Tokens

1085

Output Tokens

1019

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: Trigger was a user request to find flights London → Paris for tomorrow. We correctly resolved “tomorrow” via the calendar tool to 2025-05-29. The date matches the flights tool’s required YYYY-MM-DD format. Tools needed (calendar done; flights available) are in place, and the planned workflow is: (1) resolve date, (2) query flights, (3) compile, (4) present. We are at step 2 of 4: ready to query the flights tool. Input parameters are clear: from_city='London', to_city='Paris', date='2025-05-29'. No blockers identified; potential nuance is airport specificity, but the tool accepts city names, so city-level search is valid for the next step. After retrieving results, we’ll store them in working memory and summarize for the user.

Response: Status: On track. Next action: call the 'flights' tool with from_city='London', to_city='Paris', date='2025-05-29', then store the results and prepare a concise summary for the user.