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Clear as Glass

Detect LLM anomalies
Feed them to your evaluations

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The Silent Failure Trap

Most tools give you a good view on AI outputs
but they miss the silent failures that quietly cause real damage

Hallucination Inaccurate Response Redundant Tool Call Context Window Overflow Broken Tool Output Infinite Loop Missed User Intent Stale Data Retrieved Prompt Injection Leak Guardrail Bypass Latency Spike Token Waste

We help you detect and fix them automatically.

Build AI You Can Trust

Track every interaction, LLM call, agent chain of thought, tool usage. See user counts, costs, and latency.

Drill into every interaction with a full trace view. See each LLM call, tool use, and step in the agent's chain of thought.

Anomalies and misbehaviors are caught automatically and classified.
Their impact on users is measured and reported.

Every classified failure becomes an evaluation test case. Build a growing regression suite that fixes issues before they reach users.

Catch issues 10x faster

Automated classification surfaces problems the moment they appear. No more digging through logs.

Reduce user churn from AI failures

Quantify the cost of every failure type and fix the ones that matter most to your bottom line.

Ship changes with confidence

Run every change against a battle-tested eval suite before it reaches production.

No-Sweat Start

Start with observability in 2 minutes.
Then unlock the full flywheel

                                
python

# Install the SDK
# pip install glass-ai

import os
from glass-ai import init, interaction, traced

init(
    api_key=os.environ.get("GLASSAI_API_KEY"),
)

# Wrap your LLM interactions
with interaction(conversation_params) as trace:
# ... your LLM code here ...

# Use decorators for tool calls or other steps in your code
@traced
def search_database(query: str):
    return db.search(query)

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Your AI Agent
Clear as Glass

The Silent Failure Trap