⬡ AgentLens

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Trace Info

Run IDc5002641-ffbf-44ea-b9a1-4b0c85c8591c
Agentrag-agent
Versionv1.0
Modelqwen/qwen3.8-27b
Latency449.46ms
Tokens214 (↑184 ↓30)
Cost$0.00007106
Steps2
Status ✓ SUCCESS
Timestamp2026-08-30T06:09:56.245015

Evaluation Results

✓ PASSED 1.000 overall score
Rule-based
✅ no_error
1.0
✅ output_not_empty
1.0
✅ latency_sla
1.0
✅ output_length
1.0
✅ no_refusal
1.0
LLM Judge
✅ task_success
1.0
The agent explicitly mentioned all three required metrics: task success, coherence, and hallucination. This fully satisf...
✅ coherence
1.0
The response is concise, grammatically correct, and directly addresses the prompt by summarizing the function of the Eva...
✅ groundedness
1.0
The agent output accurately reflects the information provided in the context. The context explicitly states that 'LLM ju...
✅ hallucination
1.0
The agent output accurately reflects the known facts. It correctly identifies that LLM judges score task success, cohere...

Final Output

Based on the provided context, the Evaluation Engine uses LLM judges to score **task success**, **coherence**, and **hallucination**.

Execution Steps (2)

0
tool_call 🔧 retriever 0.05ms · 0 tokens
Input
What LLM judges does the Evaluation Engine use?
Output
Retrieved 2 docs: ['Evaluation Engine', 'AgentLens Overview']
1
llm_call 449.21ms · 214 tokens
Input
Context: [Evaluation Engine] The Evaluation Engine runs rule-based checks and LLM-judge metrics. Rules include latency SLA, output length, and refusal detection. LLM judges score task success, coherence, and hallucination. [AgentLens Overview] AgentLens is an observability platform for LLM agents. It captures traces, computes metrics, and detects regressions. It was built in 2024 by Prince Mittal. Question: What LLM judges does the Evaluation Engine use? Answer based only on the context above:
Output
Based on the provided context, the Evaluation Engine uses LLM judges to score **task success**, **coherence**, and **hallucination**.