--- title: "When AI agent traces become application data" url: https://daily.dev/posts/when-ai-agent-traces-become-application-data-nb2duz3uk source_url: https://thenewstack.io/agent-traces-application-data type: article source: "The New Stack" published: 2026-08-26T13:02:58.617Z updated: 2026-08-26T13:05:42.384Z tags: ["ai-agents", "postgresql", "observability", "clickhouse"] reading_time: 8 upvotes: 1 comments: 1 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # When AI agent traces become application data **[The New Stack](https://daily.dev/sources/newstack)** · 8 min read · 1 upvotes · 1 comments ## Summary AI agent execution traces increasingly function as application data rather than disposable telemetry, since developers and reviewers need durable, point-lookup access to individual runs while engineering teams need cohort-level analytical scans across thousands of runs. This dual read pattern strains primary transactional databases as ingestion volume grows, causing contention, analytical friction, and forced sampling. Langfuse's experience moving trace data from Postgres to ClickHouse illustrates the pattern: it hit Postgres IOPS exhaustion and multi-second query latency, then migrated tracing data to ClickHouse while keeping transactional data in Postgres, and later redesigned its data model into a wide, mostly immutable observations table, improving dashboard load times by over 10x. The guidance is to map point-lookup and cross-run scan requirements early and design storage architecture around both from the start rather than waiting for a fixed event-count threshold. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why did Langfuse move its tracing data from Postgres to ClickHouse? Langfuse moved tracing data from Postgres to ClickHouse after hitting Postgres IOPS exhaustion during ingestion and prompt API latency reaching seven seconds under heavy load. It kept transactional and latency-sensitive paths isolated in Postgres while sending trace events to ClickHouse for analytical queries, addressing contention between ingestion workload and product reads. _Teams weighing an analytical database migration for observability data can track real-world case studies like this on daily.dev._ ### What data model change improved Langfuse's dashboard performance for agent trace data? Langfuse collapsed separate trace, observation, and score tables into a single wide, mostly immutable observations table with one row per model call, tool execution, or agent step. This eliminated deduplication and cross-table joins, cutting initial table loads for large datasets from seconds to milliseconds and improving dashboard load times for large projects by at least 10 times over longer time ranges. _daily.dev surfaces data modeling patterns like this for engineers optimizing analytical storage._ ### How much event data can a single AI agent run generate? A single browser-agent session can generate hundreds of thousands of DOM diff events and run for more than 30 minutes, with Laminar reporting more than 500,000 browser events per day in one case study. For coding agents, one task can trigger multiple model calls, file reads, searches, command executions, and retries, each producing its own span or event. _Engineers sizing storage for agent observability workloads can follow findings like this on daily.dev._ ## Community discussion Top comments from developers on daily.dev. **@kartiknvj** · 0 upvotes > The dual read pattern rings true. I hit the same wall where debugging one run wants a point lookup but regression analysis wants scans over thousands, and Postgres buckled on the second. Splitting trace storage from transactional data (their Postgres to ClickHouse move) tracks with what worked for me. 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