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The modern data stack runs on code. Transformations are written in SQL, models and metrics are defined in YAML, and pipelines are orchestrated and versioned in Git.
This shift wasn’t just about syntax or tooling. It was about creating structure and intent — a shared foundation that makes data work reproducible, explainable, and collaborative. When logic lives in code, it becomes part of a living system that teams can review, test, and evolve together.
But when it comes to data reliability, many teams break that pattern. Validations and rules often live outside the codebase, defined in a UI or hidden in configuration files. It feels convenient at first, but over time those rules start to drift away from the transformations they were built to protect. Updates happen in one place but not the other, and before long, no one is sure which version reflects reality. What began as a shortcut ends up fragmenting the truth.
At Elementary, we believe the same principles that made dbt, Terraform, and Airflow successful should apply to reliability too.Code is not just a technical choice. It’s the foundation that keeps humans and systems aligned, and it’s what makes our AI agents for your reliability truly intelligent.
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Code as the Source of Truth - the Foundation for Scalable Reliability
Reliability doesn’t come from having tests. It comes from knowing those tests reflect the current state of your data, that they evolve with your models, match your business logic, and stay trustworthy as pipelines and teams grow and change.
That kind of trust doesn’t scale through documentation or manual review. It scales through code.
When reliability lives in code, it grows with your system instead of drifting away from it. Every definition, rule, and automation moves through the same lifecycle as your transformations - committed, reviewed, and versioned together.
Code gives reliability structure. It makes expectations explicit, dependencies traceable, and changes transparent. It turns reliability from a collection of checks into part of the data system itself — one that can evolve safely, auditably, and at scale.
One source of truth, many perspectives
Keeping reliability in code doesn’t mean keeping it out of reach. It means everyone can see and act on the same truth, each through their own lens.
In Elementary, the codebase is the foundation, but it’s accessible in different ways.
The Elementary UI, with built-in AI agents, lets both engineers and business users explore data health, investigate issues, and contribute to reliability without touching code.
Engineers can work directly in Git or through their code editors, while the MCP server connects that same context to tools like Cursor, so reliability insights are available right where they write and review code.
The MCP can also connect to any other environment, from ChatGPT to Claude, giving every user the flexibility to use Elementary wherever they already work.
Every action in the UI, whether by a human or an AI agent, opens a pull request. This keeps reliability transparent and collaborative while still versioned, reviewable, and controlled.
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Code makes AI agents for reliability smarter
Reliability depends on understanding context: what changed, what depends on it, and what the impact is. Most AI systems in data reliability can only react to symptoms: a failed test, a missing record, an anomaly. That’s because they lack full visibility into the logic behind it.
When reliability rules live in code, AI agents can see the full picture. They know how a model was built, what upstream tables it depends on, which validations protect it, and who owns it. With that context, they can reason about causes rather than just detect effects. They can thoroughly explain incidents, analyze test coverage, and enrich metadata.
Because they understand your pipelines at the code level, they can also see ahead. Through the Elementary MCP, AI agents can simulate the impact of changes before they reach production — predicting which tests might fail, which assets could be affected, or where data quality might degrade. It’s a preventive approach that turns reliability from reactive to anticipatory.
When AI operates this way, it stops being a layer on top of your stack and becomes part of it. It understands your logic, your expectations, and your data as they actually are, not as approximations. That grounding is what makes it accurate, efficient, and reliable.
The future of reliability
Some see code as a limitation, but in practice, it’s what creates freedom.
When everything is defined in code, you can change it safely, reuse it confidently, and scale it without losing control.
The future of reliability isn’t about choosing between code and accessibility. It’s about keeping code as the shared foundation and making it available to everyone.
When people and systems all interact with the same codebase, reliability becomes both flexible and predictable. There’s no gap between how data is built, how it’s validated, and how it’s governed.
That’s what Elementary is built for.
Code defines the truth. The UI and AI bring it to life.
Together, they make your teams faster, your systems smarter, and your data truly reliable.
The modern data stack runs on code. Transformations are written in SQL, models and metrics are defined in YAML, and pipelines are orchestrated and versioned in Git.
This shift wasn’t just about syntax or tooling. It was about creating structure and intent — a shared foundation that makes data work reproducible, explainable, and collaborative. When logic lives in code, it becomes part of a living system that teams can review, test, and evolve together.
But when it comes to data reliability, many teams break that pattern. Validations and rules often live outside the codebase, defined in a UI or hidden in configuration files. It feels convenient at first, but over time those rules start to drift away from the transformations they were built to protect. Updates happen in one place but not the other, and before long, no one is sure which version reflects reality. What began as a shortcut ends up fragmenting the truth.
At Elementary, we believe the same principles that made dbt, Terraform, and Airflow successful should apply to reliability too.Code is not just a technical choice. It’s the foundation that keeps humans and systems aligned, and it’s what makes our AI agents for your reliability truly intelligent.
Code as the Source of Truth - the Foundation for Scalable Reliability
Reliability doesn’t come from having tests. It comes from knowing those tests reflect the current state of your data, that they evolve with your models, match your business logic, and stay trustworthy as pipelines and teams grow and change.
That kind of trust doesn’t scale through documentation or manual review. It scales through code.
When reliability lives in code, it grows with your system instead of drifting away from it. Every definition, rule, and automation moves through the same lifecycle as your transformations - committed, reviewed, and versioned together.
Code gives reliability structure. It makes expectations explicit, dependencies traceable, and changes transparent. It turns reliability from a collection of checks into part of the data system itself — one that can evolve safely, auditably, and at scale.
One source of truth, many perspectives
Keeping reliability in code doesn’t mean keeping it out of reach. It means everyone can see and act on the same truth, each through their own lens.
In Elementary, the codebase is the foundation, but it’s accessible in different ways.
The Elementary UI, with built-in AI agents, lets both engineers and business users explore data health, investigate issues, and contribute to reliability without touching code.
Engineers can work directly in Git or through their code editors, while the MCP server connects that same context to tools like Cursor, so reliability insights are available right where they write and review code.
The MCP can also connect to any other environment, from ChatGPT to Claude, giving every user the flexibility to use Elementary wherever they already work.
Every action in the UI, whether by a human or an AI agent, opens a pull request. This keeps reliability transparent and collaborative while still versioned, reviewable, and controlled.
Code makes AI agents for reliability smarter
Reliability depends on understanding context: what changed, what depends on it, and what the impact is. Most AI systems in data reliability can only react to symptoms: a failed test, a missing record, an anomaly. That’s because they lack full visibility into the logic behind it.
When reliability rules live in code, AI agents can see the full picture. They know how a model was built, what upstream tables it depends on, which validations protect it, and who owns it. With that context, they can reason about causes rather than just detect effects. They can thoroughly explain incidents, analyze test coverage, and enrich metadata.
Because they understand your pipelines at the code level, they can also see ahead. Through the Elementary MCP, AI agents can simulate the impact of changes before they reach production — predicting which tests might fail, which assets could be affected, or where data quality might degrade. It’s a preventive approach that turns reliability from reactive to anticipatory.
When AI operates this way, it stops being a layer on top of your stack and becomes part of it. It understands your logic, your expectations, and your data as they actually are, not as approximations. That grounding is what makes it accurate, efficient, and reliable.
The future of reliability
Some see code as a limitation, but in practice, it’s what creates freedom.
When everything is defined in code, you can change it safely, reuse it confidently, and scale it without losing control.
The future of reliability isn’t about choosing between code and accessibility. It’s about keeping code as the shared foundation and making it available to everyone.
When people and systems all interact with the same codebase, reliability becomes both flexible and predictable. There’s no gap between how data is built, how it’s validated, and how it’s governed.
That’s what Elementary is built for.
Code defines the truth. The UI and AI bring it to life.
Together, they make your teams faster, your systems smarter, and your data truly reliable.
