> Markdown version of [/videos/100240-analytics-in-the-age-of-agentic-ai-a-tour-of-clickhouse-and-langfuse](https://www.wearedevelopers.com/videos/100240-analytics-in-the-age-of-agentic-ai-a-tour-of-clickhouse-and-langfuse). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Analytics in the Age of Agentic AI: A tour of ClickHouse and Langfuse

Are autonomous agents breaking your data architecture? Discover how unifying ClickHouse's blazing-fast analytics with Langfuse's AI observability solves the latency and security challenges of agentic AI.

- **Speakers:** [Hellmar Becker](https://www.wearedevelopers.com/@hellmar-becker)
- **Event:** World Congress 2026 Europe
- **Published:** July 10, 2026
- **Duration:** 23:38
- **URL:** https://www.wearedevelopers.com/videos/100240-analytics-in-the-age-of-agentic-ai-a-tour-of-clickhouse-and-langfuse

## Summary

The rise of agentic AI fundamentally disrupts traditional data architectures by generating unpredictable, highly concurrent workloads that standard databases struggle to process. Historically, organizations relied on siloed IT stacks for data warehousing, observability, and real-time analytics, forcing difficult trade-offs between processing scale, query speed, and data retention. As autonomous agents spawn sub-agents and simultaneously hit databases, the demand for near real-time ingestion and instant query responses becomes critical to maintaining a seamless user experience. ClickHouse addresses these bottlenecks as an open-source, columnar OLAP database that utilizes standard SQL. By leveraging columnar data compression and selective column reading, it provides the blazing-fast analytics necessary to prevent the user-facing latency common in traditional transactional databases.

With enterprises rapidly integrating AI components, managing the non-deterministic outputs and tracking the token costs of language models necessitates robust AI engineering platforms. Langfuse, an open-source AI observability tool acquired by ClickHouse, provides comprehensive trace monitoring and LLM evaluation capabilities. Instead of manually guessing agent performance, engineering teams can implement code-based evaluators or use an LLM-as-a-judge to programmatically score interactions based on helpfulness and topic adherence. Furthermore, Langfuse decouples prompt management from core application logic. By treating system prompts as versioned assets managed externally, domain experts can iterate on AI behavior independently of application developers.

Taking AI applications into production also demands stringent cost controls and data privacy measures. Langfuse and ClickHouse work in tandem to offer deep visibility into token usage while enforcing dynamic data masking architectures. This operational transparency allows organizations to safely log user prompts containing personally identifiable information (PII) by filtering sensitive entities based on contextual rules, such as masking private phone numbers while preserving public emergency contacts. Ultimately, unifying high-performance analytical storage with deep AI agent observability establishes a resilient foundation that handles the massive scale, continuous evaluation, and strict security requirements of modern software capabilities.

**Keywords:** agentic AI workloads, columnar OLAP databases, clickhouse data architecture, langfuse AI observability, LLM evaluation pipelines, system prompt versioning, token cost tracking, standard SQL analytics, high concurrency AI querying, AI agent tracing, LLM as a judge scoring, dynamic PII data masking, non-deterministic AI outputs, decoupling system prompts from code

## Chapters

1. **The impact of agentic AI on data architecture** (00:13) — Traditional siloed data structures struggle to support unpredictable workloads and the growing scale generated by AI agents.
1. **Using ClickHouse as a foundation for fast analytics** (04:42) — A fast columnar data store analyzing specific column subsets enables rapid data processing for concurrent AI responses.
1. **Live demo of AI chat interactions with ClickHouse** (08:49) — An interactive showcase reveals how prompt backend querying provides a snappy user experience for real-time assistants.
1. **Addressing AI observability and cost transparency with Langfuse** (10:28) — Visibility into model operations brings immediate clarity to token expenditures and the reliability of non-deterministic outputs.
1. **Evaluating agent interactions and managing system prompts in Langfuse** (13:05) — Abstracting system instructions away from application logic allows teams to safely refine configurations and benchmark answer quality.
1. **Summarizing the modern AI architecture with ClickHouse and Langfuse** (18:49) — Combining rapid database ingestion with robust tracing capabilities forms a highly performant and stable analytical backbone.
1. **Q&A on agent evaluation chains and PII masking** (20:03) — Fine-grained data-level masking protects personal identifiable information within tracing platforms while preserving essential context for debugging workflows.

## Related Moments

- [Architectural challenges of AI-driven data queries](https://www.wearedevelopers.com/videos/100212-olap-for-ai-applications-and-why-you-should-care) (from "OLAP for AI Applications and why you should care")
- [Overview of generative AI and the presentation agenda](https://www.wearedevelopers.com/videos/1001-langchain4j-an-introduction-for-impatient-developers) (from "Langchain4J - An Introduction for Impatient Developers")
- [Introduction to ClickHouse cloud database architecture](https://www.wearedevelopers.com/videos/100220-rate-limiting-using-ebpf-and-istio-how-to-protect-your-saas-customers-from-themselves) (from "Rate-limiting using eBPF and Istio: How to protect your SaaS customers from themselves")
- [Constructing scalable AI solutions using LangChain and LangGraph](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) (from "Building AI Applications with LangChain and Node.js")
- [Addressing audience questions on security and microservice architectures](https://www.wearedevelopers.com/videos/362-security-challenges-of-breaking-a-monolith) (from "Security Challenges of Breaking A Monolith")
- [Introduction to building real-world AI agent solutions](https://www.wearedevelopers.com/videos/1538-composable-intelligence-how-henkel-and-microsoft-are-shaping-the-agent-ecosystem) (from "Composable Intelligence: How Henkel and Microsoft Are Shaping the Agent Ecosystem")

## Related Articles

- [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)
- [WWC24 Talk - Scott Hanselman - AI: Superhero or Supervillain?](https://www.wearedevelopers.com/magazine/469-wwc24-talk-scott-hanselman-ai-superhero-or-supervillain)

## Related Jobs

- [Senior AI Agent Software Engineer (Go, Python) (m/f/x)](https://www.wearedevelopers.com/jobs/48277-senior-ai-agent-software-engineer-go-python-m-f-x) at **Dynatrace**
- [AI Software Engineer (Germany)](https://www.wearedevelopers.com/jobs/48317-ai-software-engineer-germany) at **Sunhat**
- [Data Scientist](https://www.wearedevelopers.com/jobs/ext/1351648-data-scientist) at **Almedia**
- [AI & Machine Learning Engineer (all genders)](https://www.wearedevelopers.com/jobs/48217-ai-machine-learning-engineer-all-genders) at **msg**
- [Staff Software Engineer, Copilot Experiences](https://www.wearedevelopers.com/jobs/ext/164361-staff-software-engineer-copilot-experiences) at **GitHub**
- [AI Full Stack Engineer](https://www.wearedevelopers.com/jobs/ext/1354435-ai-full-stack-engineer) at **Almedia**