We are on a mission to eliminate manual workflows, automatically increase observability and governance, and accelerate issue resolution, Enabling Data Teams to focus their Efforts on Driving Innovation.
Introducing Ella - A set of specialized AI agents that automate coverage, incident triage, data governance, data discovery, and performance optimization — without sacrificing the Elementary experience you love.
TL;DR
- Introducing Ella, a new AI layer that super‑charges Elementary with 5 agents: A test recommendations agent, a triage and resolution agent, a governance agent, a performance and cost agent and a catalog agent.
- Same developer‑first DNA: code‑native workflows, transparent logic, and an open-core architecture.
- Instant impact: fewer firefights, simplified governance, and a leaner warehouse bill — all while you stay in control.
Observability Has to Level Up: Data quality and Metadata in the AI era
There is a growing demand for data, powered by the need of organizations to harness the power of AI. In a world where everyone is using the same AI models, data is your most valuable resource. The success of your AI initiatives depends on the data you feed into the model.
However, the data itself isn’t enough. If you power your AI applications with data that has quality issues and lacks context, the model will not be able to overcome these issues. You can’t expect AI to understand your data or know if it’s trusted. Data quality, documentation, and rich metadata are the foundations for successfully leveraging data by AI models. In the AI era, Data quality isn’t a privilege, it’s a necessity.
Data Workload Is Growing. Headcount Isn’t
So the demand for higher volume and complexity of data pipelines is growing, data stacks keep scaling, but the teams that tend them do not. Many teams spend more time firefighting than innovating, and every unexpected incident steals focus from shipping value to the organization.
From day one, our focus at Elementary was on workflow. We know all data teams struggle with data quality, but it requires so much effort to solve. We believed the way to overcome this is to build tooling that developers will enjoy working with, and drive adoption of observability and governance best practices.
This promise still stands, and AI is a better implementation and how we keep it at scale.
When we think about AI‑powered observability, the goal is simple. It should eliminate manual workflows and let data teams focus on driving innovation.
Why AI-Powered Observability is the Next Step Forward
Traditional data observability platforms have helped teams monitor data quality, alert on broken pipelines, and visualize lineage. Teams often spend significant time managing rules, triaging false alarms, and hunting down metadata gaps or resource inefficiencies. These manual processes eat into productivity (and happiness).
This is why AI-powered observability matters. By integrating intelligent agents into core workflows, you gain:
- Fewer manual steps: AI eliminates repetitive tasks.
- Smarter use of resources: Al iterates and constantly optimizes coverage and configuration.
- Proactive workflows: Instead of being reactive, teams get ahead of incidents.
- Better use of expertise: Developers can focus on higher-value work, trusting the system to tackle the rest.
AI isn’t just a buzzword here. It’s a true game-changer that empowers developers by automating tedious tasks and amplifying their impact.
Meet Ella, Elementary's AI Layer
Elementary’s AI-powered observability brings a suite of specialized agents. Each tackles a different area of data operations.
Staying True to What Users Love about Elementary
While introducing new AI capabilities, we’re doubling down on what users already praise:
- Developer-first design: Every feature is built to streamline the workflow of engineering and analytics teams.
- Code‑native, version‑controlled. Define monitors, thresholds, and configuration in code, now with AI agents contributing and collaborating with you.
- Transparent & debuggable. Agents explain why they act, and you can trace their decisions.
- Open-source core. Same open‑source core, same plug‑in SDK — now extended.
These principles aren’t going anywhere. They’re the foundation of everything we build.
Ella: An Intelligence Layer, Real Benefits
Each agent is powerful alone. Together, they form Ella - an intelligence layer for data observability and operations.
- Coverage Agent spots an untested column in a critical asset and adds tests → Governance Agent suggests an owner → Triage Agent routes the incident to the right owner, with an explanation of the root cause and suggested solution.
All actions flow through Elementary’s metadata graph, so context is shared, not siloed.
This new offering leads to real, measurable advantages:
- Resolve issues faster: Incidents that used to take hours to diagnose can now be triaged and resolved in minutes.
- Reduce manual effort: Tasks like tagging, documentation, and test configuration are handled automatically, freeing your team for higher-level work.
- Boost efficiency: Smarter resource allocation and cost tracking tighten budgets and improve system resilience.
- Improve governance and coverage: Automated metadata and test suggestions drive up quality, compliance, and trust in your data.
With every release, we’re seeing more adoption and better results.
Proof & Early Results
A handful of design partners have battle‑tested the agents in production. Headlines:
- 30% increase in test coverage after an optimization sprint.
- ±40% reduction in incident triage and recovery time.
- 50% increase in governance policies implementation (docs, tags, owners).
- 3× faster onboarding for new team members (thanks to data discovery agent and automated docs).
What’s Next — Welcome to the Future of Observability
This leap forward doesn’t just incrementally improve our observability platform. By combining the power of developer knowledge with AI-driven efficiency, Elementary empowers organizations to operate with greater confidence and performance.
Unlike traditional platforms, which often require heavy manual configuration and constant vigilance, Elementary’s AI agents actively collaborate with your team. You’ll spend less time troubleshooting and more time making strategic data-driven decisions.
What sets Elementary apart:
- Touches every workflow: Detection, triage, resolution, performance, governance, and discovery.
- End-to-end: From sources, to analytics and AI pipelines.
- Interactive: Unlike other tools that stop at a single recommendation, Elementary’s agents are interactive. They extract context from you, respond intelligently, and execute tasks within your existing data observability workflows.
- Context aware: Full context from your warehouse, dbt project, and metadata, past PRs, and more.
- Add your own context: Provide extra context on how to generate documentation, use specific file names or paths, how tags have to be used, default owners, and more.
- Built-in best practices: Comes with expert logic that improves with use
- Dev-first: Every change routes through a pull request, maintaining team control
- Security & Privacy: Uses hosted models only, never extracts sensitive data, and adheres to strict AI governance policies.
- Specialized agents: Each agent was purpose-built for a specific capability
Elementary is the next generation of observability—not just because of what’s under the hood, but because of the tangible value it brings to developers and organizations.
Start Exploring AI-Powered Observability with Elementary
Are you ready to make the leap to smarter data management? Discover what AI-powered observability can do for your team:
Developers deserve observability that works as hard as they do. With Elementary AI Agents, your data operation just got a whole lot smarter—and your team just won back the hours to prove it.
We are on a mission to eliminate manual workflows, automatically increase observability and governance, and accelerate issue resolution, Enabling Data Teams to focus their Efforts on Driving Innovation.
Introducing Ella - A set of specialized AI agents that automate coverage, incident triage, data governance, data discovery, and performance optimization — without sacrificing the Elementary experience you love.
TL;DR
- Introducing Ella, a new AI layer that super‑charges Elementary with 5 agents: A test recommendations agent, a triage and resolution agent, a governance agent, a performance and cost agent and a catalog agent.
- Same developer‑first DNA: code‑native workflows, transparent logic, and an open-core architecture.
- Instant impact: fewer firefights, simplified governance, and a leaner warehouse bill — all while you stay in control.
Observability Has to Level Up: Data quality and Metadata in the AI era
There is a growing demand for data, powered by the need of organizations to harness the power of AI. In a world where everyone is using the same AI models, data is your most valuable resource. The success of your AI initiatives depends on the data you feed into the model.
However, the data itself isn’t enough. If you power your AI applications with data that has quality issues and lacks context, the model will not be able to overcome these issues. You can’t expect AI to understand your data or know if it’s trusted. Data quality, documentation, and rich metadata are the foundations for successfully leveraging data by AI models. In the AI era, Data quality isn’t a privilege, it’s a necessity.
Data Workload Is Growing. Headcount Isn’t
So the demand for higher volume and complexity of data pipelines is growing, data stacks keep scaling, but the teams that tend them do not. Many teams spend more time firefighting than innovating, and every unexpected incident steals focus from shipping value to the organization.
From day one, our focus at Elementary was on workflow. We know all data teams struggle with data quality, but it requires so much effort to solve. We believed the way to overcome this is to build tooling that developers will enjoy working with, and drive adoption of observability and governance best practices.
This promise still stands, and AI is a better implementation and how we keep it at scale.
When we think about AI‑powered observability, the goal is simple. It should eliminate manual workflows and let data teams focus on driving innovation.
Why AI-Powered Observability is the Next Step Forward
Traditional data observability platforms have helped teams monitor data quality, alert on broken pipelines, and visualize lineage. Teams often spend significant time managing rules, triaging false alarms, and hunting down metadata gaps or resource inefficiencies. These manual processes eat into productivity (and happiness).
This is why AI-powered observability matters. By integrating intelligent agents into core workflows, you gain:
- Fewer manual steps: AI eliminates repetitive tasks.
- Smarter use of resources: Al iterates and constantly optimizes coverage and configuration.
- Proactive workflows: Instead of being reactive, teams get ahead of incidents.
- Better use of expertise: Developers can focus on higher-value work, trusting the system to tackle the rest.
AI isn’t just a buzzword here. It’s a true game-changer that empowers developers by automating tedious tasks and amplifying their impact.
Meet Ella, Elementary's AI Layer
Elementary’s AI-powered observability brings a suite of specialized agents. Each tackles a different area of data operations.
Staying True to What Users Love about Elementary
While introducing new AI capabilities, we’re doubling down on what users already praise:
- Developer-first design: Every feature is built to streamline the workflow of engineering and analytics teams.
- Code‑native, version‑controlled. Define monitors, thresholds, and configuration in code, now with AI agents contributing and collaborating with you.
- Transparent & debuggable. Agents explain why they act, and you can trace their decisions.
- Open-source core. Same open‑source core, same plug‑in SDK — now extended.
These principles aren’t going anywhere. They’re the foundation of everything we build.
Ella: An Intelligence Layer, Real Benefits
Each agent is powerful alone. Together, they form Ella - an intelligence layer for data observability and operations.
- Coverage Agent spots an untested column in a critical asset and adds tests → Governance Agent suggests an owner → Triage Agent routes the incident to the right owner, with an explanation of the root cause and suggested solution.
All actions flow through Elementary’s metadata graph, so context is shared, not siloed.
This new offering leads to real, measurable advantages:
- Resolve issues faster: Incidents that used to take hours to diagnose can now be triaged and resolved in minutes.
- Reduce manual effort: Tasks like tagging, documentation, and test configuration are handled automatically, freeing your team for higher-level work.
- Boost efficiency: Smarter resource allocation and cost tracking tighten budgets and improve system resilience.
- Improve governance and coverage: Automated metadata and test suggestions drive up quality, compliance, and trust in your data.
With every release, we’re seeing more adoption and better results.
Proof & Early Results
A handful of design partners have battle‑tested the agents in production. Headlines:
- 30% increase in test coverage after an optimization sprint.
- ±40% reduction in incident triage and recovery time.
- 50% increase in governance policies implementation (docs, tags, owners).
- 3× faster onboarding for new team members (thanks to data discovery agent and automated docs).
What’s Next — Welcome to the Future of Observability
This leap forward doesn’t just incrementally improve our observability platform. By combining the power of developer knowledge with AI-driven efficiency, Elementary empowers organizations to operate with greater confidence and performance.
Unlike traditional platforms, which often require heavy manual configuration and constant vigilance, Elementary’s AI agents actively collaborate with your team. You’ll spend less time troubleshooting and more time making strategic data-driven decisions.
What sets Elementary apart:
- Touches every workflow: Detection, triage, resolution, performance, governance, and discovery.
- End-to-end: From sources, to analytics and AI pipelines.
- Interactive: Unlike other tools that stop at a single recommendation, Elementary’s agents are interactive. They extract context from you, respond intelligently, and execute tasks within your existing data observability workflows.
- Context aware: Full context from your warehouse, dbt project, and metadata, past PRs, and more.
- Add your own context: Provide extra context on how to generate documentation, use specific file names or paths, how tags have to be used, default owners, and more.
- Built-in best practices: Comes with expert logic that improves with use
- Dev-first: Every change routes through a pull request, maintaining team control
- Security & Privacy: Uses hosted models only, never extracts sensitive data, and adheres to strict AI governance policies.
- Specialized agents: Each agent was purpose-built for a specific capability
Elementary is the next generation of observability—not just because of what’s under the hood, but because of the tangible value it brings to developers and organizations.
Start Exploring AI-Powered Observability with Elementary
Are you ready to make the leap to smarter data management? Discover what AI-powered observability can do for your team:
Developers deserve observability that works as hard as they do. With Elementary AI Agents, your data operation just got a whole lot smarter—and your team just won back the hours to prove it.
