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
15s
Input Tokens
1085
Output Tokens
359
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 user goal: find flights London → Paris for tomorrow.
2. Calendar tool already run; tomorrow’s date obtained as 2025-05-29.
3. Remaining plan (per scratch_pad):
   • Use flights tool with params {from_city:'London', to_city:'Paris', date:'2025-05-29'}
   • Store results, then compile final answer.
4. Current progress says we are on step 2 of 4, exactly matching the plan timeline (interpret date before querying flights).
5. No blockers or deviations detected; required tools are available and date format matches tool schema.
Conclusion: execution is proceeding as expected; next immediate action is to call the flights tool.

Response: We are on track. Proceed to the next action: invoke the flights tool with {from_city: 'London', to_city: 'Paris', date: '2025-05-29'}.