Turn Every AI Failure
Into Your Next Fix

The continuous improvement flywheel for AI agents. From observability to optimization, in one platform.

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Glass dashboard interface
Glass dashboard interface - analytics view
Step 1

Full-Stack Agent Observability

Track every interaction, LLM call, agent chain of thought, tool usage. See user counts, costs, and latency. All in real-time.

  • Every LLM call, tool use & chain-of-thought traced
  • User sessions, costs & latency at a glance
  • Custom metadata & filtering
Dashboard
USAGE
Interactions 24,847 ↑ 12.3%
Users 1,205 ↑ 8.1%
Sessions 6,391 ↑ 15.4%
Success rate 97.8% ↑ 1.2%
LATENCY
Average 22s ↓ 18.6%
p50 19s ↓ 22.1%
p95 27s ↓ 9.7%
p99 31s ↑ 3.2%
Token Usage by Model
Total: 48.2M Cost: $127.40
MODEL TOKENS IN TOKENS OUT COST
gpt-4o 21.4M 3.8M $89.10
claude-sonnet-4-20250514 12.6M 2.4M $31.50
Step 2

Intelligent Failure Classification

Failures and misbehaviors are caught automatically and classified: hallucinations, inaccuracies, redundant tool calls. Their impact on users is measured and reported.

  • Auto-detection of hallucinations, inaccuracies, loops
  • User impact scoring (churn cost, support cost)
  • Daily failure ingest with severity breakdown
Failure Classification
Hallucination 23 users affected
Critical $1,840
Redundant tool calls 156 sessions
Warning $920
Off-topic response 8 users affected
Info $640
Est. churn cost $3,400/mo
Step 3

Failures Become Evals

Every classified failure is automatically converted into an evaluation test case. Build a growing eval suite that catches regressions before they reach users.

  • Failures auto-converted to eval test cases
  • Growing regression suite, always up to date
  • Run evals on every prompt or model change
Eval Suite / 847 test cases
TEST CASE SOURCE STATUS
Hallucination #412 Interaction #8291 Pass
Inaccuracy #89 Interaction #7104 Fail
Tool loop #23 Interaction #9482 Pass
Coverage 94.2%
Regressions caught before deployment
Step 4

Prompt Management & Optimization

Version your prompts, test fixes against your eval suite, and deploy improvements with confidence. Then monitor again. The flywheel keeps turning.

  • Prompt versioning & diff view
  • Test changes against your eval suite before deploying
  • Track improvement over time
Prompt v2.4 → v2.5
- You are a helpful assistant. Answer questions accurately.
+ You are a helpful assistant. Answer questions accurately. If unsure, say "I don't know" rather than guessing.
  Always cite sources when available.
Eval Results 842/847 passing (99.4%)

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 prompt 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 
# 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)

Start Your AI Improvement Flywheel

From observability to optimization, in one platform

Book a demo