A few weeks ago we launched dltHub Pro. What surprised us was what people built with it. Teams didn't ask for primitives, they asked for complete solutions. So we built Blueprints.

We are launching two dltHub Blueprints for agent spend: Agent Cost & Usage to understand it, Agent Distillation to optimize it

dltHub launches two Blueprints for agent spend: Agent Cost & Usage to break down what each model, person, and customer costs, and Agent Distillation (with distil labs) to replace expensive agents with cheaper specialist models.

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How do you eval your agents? Agent traces are a good place to start. In this 1-hour workshop with @DataTalksClub, you'll learn how to ingest agent traces, model nested JSON into a queryable schema, and build dashboards to understand agent behavior.

Ingesting Agent Traces with dlthub - Alena Astrakhantseva

Links:- https://test-agent-traces-api-xt2e7ottma-ew.a.run.app/doc...

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AI writing pipelines is cool. AI remembering the entire workflow is better. dltHub Pro carries context from ingestion to deployment to maintenance instead of starting over every step. Try it: https://dlthub.com/products/dlthub

AI agents generate traces, but are you analyzing them?  Join Alena Astrakhantseva to learn how to turn tool calls, token usage, and outcomes into structured, queryable data with dltHub Pro. July 6, live on YouTube with DataTalks.Club Register ↓

Ingesting Agent Traces with dlthub · Luma

In this hands-on workshop, we'll show you how to stop flying blind on your AI agents. Using dltHub Pro we'll build a pipeline that ingests agent traces (e.g.…

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Text-to-SQL doesn't break because models can't write SQL, it breaks because they don't know what your data means. An agent can return valid SQL and still be wrong, and a clean wrong number looks just as trustworthy as a right one.

Human in the loop shouldn't mean copy-pasting context into an agent every 5 minutes. It should mean judgment, not errands. If your agent needs a Rube Goldberg machine of prompts, tabs, and slack messages, the problem isn't human. It's missing context. https://dlthub.com/blog/context

Gave an LLM a schema: 3/10. 📉 Gave it a schema + an ontology: 10/10. 🎯 Same model, same data. The difference? It finally understood what the columns actually mean instead of just vibing off the names.

91% of the 81,000 new dlt pipelines shipped in January 2026 were built by agents. The bottleneck in data engineering is no longer implementation, it's meaning.

How much data can $1 of compute move? We benchmarked dltHub on a small worker (2 vCPU / 4 GB) loading into BigQuery: Parquet: ~170 GB Postgres: ~65 GB JSON: ~4.6 GB REST: whatever the API allows Methodology + results ↓ https://dlthub.com/blog/benchmark-dlthub

Data quality usually starts too late. By the time bad data hits a dashboard, the original business assumptions are gone. We're fixing that at the ingestion layer. 👇

Agentic Analytics Demo Night lands in San Francisco this Wednesday 🚀 We're joining @inngest.com, lightdash, and @streamkap.bsky.social for an evening of demos from teams building at the intersection of AI, analytics, and data infrastructure. 🧵

What does it take to build a product people actually keep using? On Wednesday, we're hosting Sameer Alsakran, Founder & CEO of @metabase.com, at our office in Berlin for a live conversation with Francesco Mucio from Data Berlin. 🧵

Berlin’s Applied AI Week is looking pretty special. Tomorrow, dltHub and @modal-labs.bsky.social are hosting an evening in Berlin for founders, engineers, and builders shipping agents in production. Featuring Kenny Ning, Jefferson Girao & Alena Astrakhantseva.

The AI stack is evolving fast, but reliable data movement is still the foundation. Join dltHub, LanceDB and DataHub on May 21 in Menlo Park for talks on multimodal AI storage, AI data pipelines, and trusted lineage systems. 🔗

The missing data layer for ML: dltHub x LanceDB x DataHub @ SVAI · Luma

Modern problems = Modern solutions! Join dltHub, LanceDB, and DataHub for a night of technical talks and demos. Hear from the engineers building the ingestion,…

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How fast can you go from zero to a production-ready data pipeline when AI is your copilot? Next Wednesday, Elvis Kahoro joins @temporal.io alongside Melanie Warrick and @cecilphillip.bsky.social for a live Vibe Check building a GitHub-powered pipeline with AI + dlt.

Building pipelines with AI usually means losing context between tools. dltHub AI Workbench runs the full 12-step workflow as a continuous session, schemas, incrementals, traces, transformations, and notebooks share context across the stack. https://dlthub.com/blog/agentic-data-engineering-course

Explainer on ontology engineering and what we're building around it: why just clean schemas and prompts aren’t enough, and how adding a canonical model + taxonomy + ontology changes what agents can correctly compute (ARPU being the clearest example). 

Ontology engineering: what it is, why it's back, and why agents need it

Agents don't hallucinate. They navigate without a map. Ontology engineering is how you build one, and why every team pulling humans out of the loop needs it now.

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We’re excited to share that Violetta Mishechkina will be speaking at GOSIM Paris 🇫🇷 Invited by probabl.ai to join the “Own Your Data Science and AI” workshop. 🎤 From Agent Traces to Analytics Agents generate code, text, telemetry, yet most teams still rely on stale datasets.

Skills that wrap a library are software. They have dependencies, need maintenance, and degrade when the product changes and no one updates them. The vendor owns the product surface. You own your integration. Same rule, new layer.

1/ dlt is now available as a Snowflake Native App. Replicate MSSQL, MySQL & PostgreSQL → Snowflake, without leaving Snowflake. Create, schedule, and monitor pipelines from the Snowflake UI. No external orchestrator. https://app.snowflake.com/marketplace/listing/GZ1MMZ1IPICU/dlthub-dlt-connector-app

Every layer of software automation was called overkill before it became the baseline. Fortran → Make → CI/CD → Docker → now agents. Code that runs is only the 10%. The other 90% is engineering judgment, boundaries, and iteration.

AI agents don’t just use your APIs, they optimize around them. Ask an agent to “build a pipeline” and it will find credentials, escalate privileges, and take the shortest path to completion. Not against your interests, just goal-driven.

Not everything that can be modeled should be. With LLMs, more context doesn’t mean a better prompt. The key is Minimum Viable Context for high-precision data models. Here’s what we learned building ontology-driven modeling. Blog by Hiba Jamal ↓

Minimum Viable Context for Building a Canonical Data Model

Call it the MVC problem: minimum viable context. Too little and it hallucinates your domain. Too much and it drifts from your actual goal. The process has to be controlled.

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AI can generate a data pipeline in 10 minutes. But can you trust what it produces? That’s the real problem, and it’s not technical. It’s business. If you can’t trust the output, it never reaches production. 🧵

Most AI coding tools stop at “here’s your code.” But getting pipelines into production, and trusting them there, is the hard part. We built a deployment toolkit that closes that gap 🧵

Most teams still build connectors from scratch, one at a time. Different patterns. Different implementations. Accumulating tech debt. What if you built the system instead? We just released dlt Skills, a pipeline factory powered by Claude. 👇

LLMs fail at data transformation because they see isolated tables, not your business. We built the dltHub AI Workbench transformation toolkit to fix that. You feed it sources + use cases → it builds a taxonomy, business ontology, and a Canonical Data Model.

The craziest part of the new dltHub AI release? The MCP integration. Asked Claude Code for an OpenAI pipeline -> it searches the dlt context -> scaffolds the exact code with schema & incremental loading. No more starting from scratch. https://dlthub.com/blog/ai-workbench

New course: Agentic Data Engineering with dltHub 🤖 Agents can now write entire data pipelines, but writing code was never the hard part. The real challenge? Data quality, schema stability, and running reliably in production. 🧵👇

Agents can generate data pipelines. The real problem now is trusting them in production. Today we’re introducing the dltHub AI Workbench, infrastructure for generating, validating, and deploying pipelines in one workflow.