The GlassFlow blog

How to debug an AI agent in production: find the run, trace it to the first divergence, classify the failure mode, and ship a fix. A step-by-step guide." slug: "blog/what-is-agent-observability

The seven ways AI agents fail in production: tool misuse, context loss, goal drift, retry loops, frozen agents, cascading errors & silent degradation.

When an AI agent fails in production, it's usually reasoning correctly over stale, partial, or lossy inputs, not hallucinating. Why, and how context fixes it.

The modern data stack was built for human analysts. AI agents read, push, and shape the model themselves. Here's why they need a new kind of data layer.

What it really costs to self-host Langfuse, Phoenix, or Laminar: ClickHouse and infra requirements, engineering hours, and real AWS pricing, single node vs. high availability

The barrier to autonomous agents isn't trust. It's the missing infrastructure. Here are the six pieces the short-session era never built for long-running agents.

Build a 5G RAN telemetry pipeline with OpenTelemetry and GlassFlow to deduplicate metrics, normalize vendor schemas, and detect signal degradation in ClickHouse

GlassFlow v3.1.0 and v3.2.0 bring end-to-end backpressure handling, resilient ClickHouse retries, and full pipeline observability.

A technical comparison of OpenTelemetry → Kafka → ClickHouse vs OpenTelemetry → GlassFlow → ClickHouse. Learn where Kafka adds real value and where it adds operational overhead your observability pipeline doesn't need.

Duplicate rows, broken JOINs, too many parts errors. These ClickHouse mistakes cost teams weeks. Here's what causes them and how to fix them.

This guide shows you how to stream OTLP data, perform deduplication and PII masking with GlassFlow and send data directly into ClickHouse for high-performance, low-cost observability.

GlassFlow now supports native OpenTelemetry ingestion — so your traces, logs, and metrics arrive in ClickHouse deduplicated, enriched, and query-ready

A demo using GlassFlow to deduplicate and filter Kafka events before they reach ClickHouse for real-time fraud detection.

Teams kept asking if Glassflow would scale, so we rebuilt it to grow from small to massive workloads without changing your pipeline.

Turn messy logs into analytics-ready data with GlassFlow & ClickHouse.

Benchmarking GlassFlow: Fast, reliable deduplication at 20M events

This is what happens when vertical scaling becomes your bottleneck

Learn how AI systems push Kafka to ClickHouse beyond what most pipelines were built for.

Managing AI backfills safely in ClickHouse clusters.

Build a complete observability pipeline from telemetry to insights.

Build real-time analytics with Kafka and ClickHouse.

Remove duplicate events before they reach ClickHouse.

ReplacingMergeTree deduplication in ClickHouse – and its limitations.

Deduplication with GlassFlow: Fixing the limits of ClickHouse merges

We compared GlassFlow and ClickPipes for ClickHouse deduplication

Understand and compare ClickHouse’s AggregatingMergeTree engine

Open-source observability stack: ClickHouse, GlassFlow & Coroot.

Open source observability with ClickStack and GlassFlow

How ClickHouse powers observability at AI scale: Fast, efficient and ready for growth

When Kafka fits and when alternatives make sense.

Compare Snowflake and ClickHouse for modern analytics.

Migrating from Snowflake to ClickHouse: Step-by-Step Guide

Compare Amazon Redshift and ClickHouse for modern analytics.

Migrating from Redshift to ClickHouse: Step-by-Step Guide

Migrating from Bigquery to ClickHouse: Step-by-Step Guide

Learn ClickHouse JOINs step by step: types, examples & best practices.

ClickHouse doesn't handle JOINs like traditional databases. We explain why (rule-based planning, no data shuffling, memory limits) and what to do about it.

Why denormalization slows down ClickHouse and what to do instead.

Struggling with JOINs in ClickHouse? Learn how to handle them efficiently.

Solve ClickHouse JOIN limitations with GlassFlow.

Speed up ClickHouse queries using keys, indexes, dedup & JOIN optimization

Materialized Views in ClickHouse: Benefits and Limits

How to fix common problems with Materialized Views in ClickHouse

From data lakes to ClickHouse: mastering real-time data transformation.

A practical overview of the ClickHouse ecosystem and its key tools.

Managed ClickHouse services compared by features, pricing and use case.

Set up real-time ETL pipelines to ClickHouse with GlassFlow and Docker.

Deduplicate streaming logs before ClickHouse-no code, no duplicates.

Joining Kafka Streams Before ClickHouse with GlassFlow

Simplify Cloudflare’s ClickHouse log analytics with GlassFlow: faster, cleaner.

Simplify Kafka-to-ClickHouse Pipelines with GlassFlow and Altinity.Cloud

Understand asynchronous inserts in ClickHouse with a practical example.

Which Kafka-to-ClickHouse method is right for your stack?

Why integrating Flink with ClickHouse is difficult – key challenges explained

Flink + ClickHouse: Integration challenges explained.

Compares Flink alternatives for real-time ClickHouse integration.

Why companies build custom Go ingestion services for ClickHouse pipelines.

Stream Bitcoin to ClickHouse in real time-without duplicate records

Learn how we will solve the biggest challenges of Kafka users with ClickHouse.

Deep dive on raw data ingestions from Kafka to ClickHouse.

Learn the root of the duplication and JOINs issues of Kafka to ClickHouse.

Deep dive on limitations of ReplacingMergeTree and Materialized Views.

Apache Flink isn't the solution for duplications and JOINs on ClickHouse.

Learn the details on how GlassFlow will solve Duplications and JOINs.

ClickHouse Glossary: Key terms for engines, joins & real-time data

GlassFlow has successfully raised its seed round. Learn more about our journey.

Real-time Price Recommendations Using AI and Data Transformation

Real-time Clickstream Analytics Using AI and Data Transformation

Data anomaly detection and alerting using GlassFlow, OpenAI, and Slack.

Car Sharing Fleet Management with Real-time Fuel Monitoring and Discounts

Enhancing Airbnb Listings with Real-time Generative Feedback Loop Automation

Explore this complete anomaly detection guide

How the most popular tools fit the needs of AI startups

Supercharge Your Classified Ads with Real-time Data Enrichment

Event-driven data pipeline is the secret sauce for AI application success

GlassFlow is offering managed connectors for NATS users

Real-time data streaming pipelines with GlassFlow’s managed Webhook connectors

Real-time analytics and fast decision-making with GlassFlow and ClickHouse

Fully Managed Connector Integration for Google Pub/Sub

Fully Managed Data Source Integration for Amazon Simple Queue Service

Integrating real-time data processing into Zapier

Leverage real-time data streams for dynamic pricing, location-based offerings

An introduction to data pipeline tools and how to choose the right one

Latest trends in data infrastructure: from real-time processing to AI insights

Tools, best practices, and real-time use cases for efficient data processing

Discover how to use Python for ETL processes.

Discover the key differences between batch processing and stream processing.

Explore what real-time analytics is.

Learn what a machine learning pipeline is: best practices, use cases, and tools.

How Change Data Capture (CDC) enables real-time data replication & integration.

Learn the real-time streaming basics for your data pipeline.

Build a full-stack event-driven chat application using Nats, Streamlit

Build a real-time AI-powered alerting app using OpenAI, GlassFlow and Streamlit

Explore how GlassFlow fits into the modern data stack

Why Java Struggles with AI and Machine Learning, why Python shines.

Understanding Anomaly Detection: Key Techniques and Use Cases

A step-by-step guide how you build a data synch stack with popular technology.

What is Change Data Capture (CDC) and how it works?

How to detect and process a constant stream of data

How GlassFlow helps startup builders using low-code and no-code tools

Learn how to build data pipelines with this step-by-step guide.

Leveraging GlassFlow for Real-time Predictive Maintenance

Predict customer churn and deploy effective retention strategies.

Leveraging GlassFlow for Real-Time Data Transformation

Real-time data transformation to enhance your marketing strategies.

Leverage GlassFlow for real-time data transformation to improve user engagement

Create personalized online learning experience

Mask Personally Identifiable Information (PII) using GlassFlow

Ensuring the integrity and accuracy of your data streams with GlassFlow

Unlock the Power of Real-Time Data Transformation with GlassFlow

Rethinking the Data Stack for Real-Time Needs

Stream Postgres data to Snowflake in real time with CDC.

Why GlassFlow stands out for event-driven data pipelines

Top 10 challenges Data Engineers and Data Scientists face.

Python Streaming Database vs. Stream Processing Frameworks

Comparing GlassFlow and AWS Lambda

A fresh new design for the GlassFlow WebApp and Jupyter Notebook examples.