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

1091

Output Tokens

243

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. Review the current state: The user asked for flights from London to Paris for tomorrow. Cycle 3 is running. Calendar tool already returned 2025-05-29 as tomorrow’s date. Remaining goals are (a) call the flights tool for London→Paris on 2025-05-29 and (b) present the results.
2. Check prerequisites: We have the required date (2025-05-29 in YYYY-MM-DD format) and valid cities (London, Paris). The flights tool is available and ready.
3. Determine progress: Step 2 of 4 is complete; next logical step is to invoke the flights tool.
4. Conclusion: All prerequisites are met, no blockers exist, and the next action is clearly defined. Therefore we are on track to complete the task in the next cycle.

Response: We are on track to complete the task. The calendar tool has provided tomorrow’s date (2025-05-29), and we are ready to invoke the flights tool to search for available flights from London to Paris on that date, followed by presenting the results to the user.