Blog
Insights on agentic data engineering and how modern platforms are opening the web's data.
Knowledge BaseThe many places AI agents go for business context
An AI agent can make a remarkably thorough tour of your company before writing a single line of SQL. After all those stops, it may still be guessing what “on-time delivery” means.
Priyaanka AroraOct 15, 2026
Knowledge BaseA coding agent skill is not a substitute for business context
Data teams can package engineering practices into reusable agent skills that can make coding agents more systematic, but ultimately data pipeline correctness depends on another category of business context: a spec outlining logic that defines how to build.
Priyaanka AroraOct 7, 2026
Knowledge BaseOct 2, 2026Spec-Driven Development with Snowflake CoCo
If any data coding agent could get away without a spec, it's the one that already knows your warehouse: Snowflake CoCo. So, does it stack up?
Knowledge BaseSep 11, 2026How Spec-Driven Development Maximizes dbt Wizard
Here’s why I think dbt Wizard makes the case for spec-driven development stronger. Agents made implementation cheap. Specs are what make correctness checkable. And in agentic data engineering, you want both.
Knowledge BaseSep 3, 2026Semantic layer vs context layer: what AI agents need first
A semantic layer gives AI agents governed metrics. A context layer adds lineage, policy, provenance, and operational state. Both depend on business decisions someone has resolved and recorded.
Knowledge BaseAug 28, 2026Reverse-engineering a chart of accounts you inherited
A data team has to turn accumulated judgement into a repeatable system. A dashboard answers one question, but the record of how those numbers are defined improves every financial data project that follows.
Knowledge BaseAug 28, 2026The case for spec-driven development in analytics engineering
Spec-driven development can give agents the structure to find the business rules, resolve the open decisions, define what correct means, then build against the spec.
Knowledge BaseAug 10, 2026The spec is the new source code
When implementation becomes abundant, the bottleneck moves upstream: can you describe business rules precisely enough for an agent to build with all the right assumptions baked in?
Knowledge BaseJul 30, 2026How to steer coding agents amid rising token costs
How should engineering teams get more out of every agent run? Through two jobs: reading the trace of agent activity, and knowing the organization’s specific business logic well enough to minimize agent prompt loops.
Knowledge BaseJul 21, 2026Before you work with a coding agent, talk to Robert
"Value creation comes from Robert, who has been here for twenty years and has never documented anything." Every business has a Robert you should go talk to before automating code that relies on accurately capturing your business logic.
EventsJun 26, 2026Join us in Montreal: Your data stack is going agentic
A panel of senior data engineering practitioners and leaders will break down where agentic AI is delivering results across platforms like Snowflake, Databricks, and Microsoft Fabric, and how teams are rethinking data pipeline design and transformation workflows in tools like dbt.
EventsJun 23, 2026Snowflake Summit in 60: Montreal data community event recap
Last week, we hosted a Summit in 60 Special Edition at the Snowflake Montreal User Group. Over 60 practitioners from across the Montreal data community joined us for an evening of open discussion over food and drinks.
Knowledge BaseJun 15, 2026A data pipeline that argues for its own correctness
Verification has always been part of data engineering: profiling columns, testing keys with dbt, checking how tables relate. With a self-verifying system, what’s new is where the checks come from: derived directly from the business logic behind a transformation and generated alongside the pipeline, instead of added by a human afterward.
AnnouncementMay 29, 2026Introducing Maxa Autopilot: The Agentic Way to Build and Maintain Data Pipelines
Maxa Autopilot is an agentic platform that turns your business context, requirements, and source system data into production-ready dbt pipelines tailored to your specific rules, systems, and reporting needs.