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

13s

Input Tokens

1195

Output Tokens

705

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: Let me assess the current state and trajectory:

**Current Status Analysis:**
- We are in cycle 3, step 2 of a 4-step process
- Original task: User requested flights from London to Paris for tomorrow
- Progress made: Successfully determined tomorrow's date (2025-05-29) using calendar tool
- Next planned action: Use flights tool to search for available flights
- Available tools are all functioning (calendar, flights, web_search)

**Trajectory Review:**
The execution follows a logical, efficient path:
1. Received user request
2. Identified need to determine date (calendar tool) ✓
3. Ready to search flights (flights tool) ← Current position
4. Will present results to user

**Assessment:**
✅ **WE ARE ON TRACK**

The agent is executing the plan correctly with appropriate tool selection, logical step progression, and clear goal alignment. The progress matches expectations for the current cycle, and all necessary tools are available and functional. No deviations or issues detected.

Response: We are on track. The execution is following the planned 4-step trajectory efficiently: (1) user request received ✓, (2) date determination completed ✓, (3) flight search ready to execute, (4) results presentation pending. Current progress (step 2 of 4) is appropriate, tools are functional, and the next logical action (using flights tool) is clear.