My pipeline inspector got hands: retrying and fixing failed dltHub jobs with a Cortex agent
Last time the agent only read logs and wrote a diagnosis. Now it pushes commits to master 🤖
Snowflake's AI_PARSE_DOCUMENT got an agentic mode
12x the price of OCR per page, and it earned that on one file type
The dltHub REST API updated yesterday
No OpenAPI endpoint. So I unzipped the client library and diffed it.
Asking small decision models (Jev + Laya) about first names
Thousands of contacts without salutation, and a model that has to guess from a first name alone.
Towards self-healing data pipelines: dltHub inspector in ❄️ Cortex
A Snowflake Cortex Agent for dltHub jobs
Where dltHub's AI harness ends and Snowflake's CoCo begins
dltHub's AI harness writes the pipeline, Snowflake's CoCo takes over after data landed. Together they ✨
The regexes had a good run
Hand-written patterns decided which Salesforce errors were noise. Now a classifier does it, and it says how sure it is.
Making Sonnet 5 the CoCo default
Cortex Code's model default? Turns out it's just a CREATE AGENT statement away.
Snowflake gave everyone 102 models and didn't tell me
200 users, zero curation, and a bootstrap grant I didn’t ask for.
What if a dltHub pipeline fails without errors?
A watchdog to keep all dlt pipelines in line
70 dlt pipelines, zero comments (and no alert when the schema changed)
Nobody documents dozens of tables with hundreds of columns by hand. So an agent does it, INFORMATION_SCHEMA keeps it honest, and Teams pings when a source API adds a column
Snowflake data movement policies: the copy path gets a guardrail
Masking decides what you can see. Data movement policies decide what you can take.