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The 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.

A 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.

Spec-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?

How 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.

Semantic 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.

Reverse-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.

The 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.

The 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?

How 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.

Before 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.

A 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.

What is Data Harmonization? (And Why Your Business Needs It)