Your Systems Keep Changing. AI Helps Your Data Keep Up.
A new app, an ERP upgrade or a renamed field can disrupt the data behind your decisions. Rudder Analytics uses AI agents to build and maintain your pipelines, with engineers approving changes before release.
Watching every layer
Where the Data Team’s Week Goes
of engineering time goes to maintaining existing pipelines.
4.7pipeline failures per month on average
13hours per incident, approximately
Those hours come out of the roadmap: new sources, new dashboards and AI projects waiting on clean data.
Source: Fivetran, The Enterprise Data Infrastructure Benchmark Report 2026. A survey of 500 senior data and technology leaders at companies with more than 5,000 employees, Q4 2025.
Where AI Agents Sit in Your Data Stack
The AI agents work alongside your data platform and code repository. Rudder Analytics configures the connections and access they need.
Data moves from source systems through ingestion, transformation and warehouse or lakehouse layers to reports and AI applications. AI agents receive operational signals, metadata and relevant incident history. A lead agent coordinates specialist roles using approved models and scoped tools. Proposed changes are tested in an isolated environment before entering the repository review workflow. Automated checks and engineer approval precede production deployment. Only affected jobs and layers are updated, in dependency order where required. Post-release checks validate data and pipeline health. Evidence and approvals are recorded, and verified outcomes can inform later tasks. Access policies, privacy controls and budgets apply throughout. Teams can begin with observation and expand to additional workflows as results are validated.
Your Data Platform
The AI Agent Team
Scoped production reads · isolated test writes · controlled tool access
Guardrails & Accountability
Engineer-Approved Delivery
Applied Level by Level
The Data Engineering Work AI Takes On
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Schema Changes
The agent checks how new columns, type changes and removed fields affect downstream data. It proposes updates across the affected layers using your business rules.
Agentic Schema Evolution -
Data Testing
The agent drafts tests for new tables and updates them as business logic changes. Quality checks flag or block records that fail your rules before they reach reports.
AI Agent for Testing -
Failure Diagnosis
The agent reviews failed jobs and recent changes to identify likely causes. Engineers receive the evidence and a proposed fix for review.
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New Pipelines
The agent uses your source mappings to draft loading code, transformations and documentation. Engineers refine the work to bring new data into use sooner.
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Cloud Data Costs
The agent finds the queries and jobs that drive your cloud data bill. Each rewrite must return identical results before release.
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Platform Migration
The agent helps convert legacy SQL and ETL jobs for your new platform. Engineers check the converted logic and reconcile outputs before the switch.
A Pull Request Before Anything Goes Live
The agent never edits production directly. Its work arrives in your GitHub or Bitbucket repository as a pull request. The code, the test results and the affected layers sit together for review.
Carry Shopify’s sales channel field through the data layers
bronze/+4shopify_orders.sql silver/+6−2orders.sql gold/+5−1fct_sales.sql marts/+12channel_revenue.sql tests/+18orders.yml
- 24 data tests passed
- Revenue totals reconcile across layers
- 3 dashboard queries passed
DLApproved by your data lead
Released in the order data flows
- BronzeRaw copies of each source
- SilverCleaned and joined
- GoldBusiness-ready metrics
- Data MartsViews for each department
- ReportsChecked after refresh
- The agent cannot merge its own pull request.
- A failed check halts the rollout at that layer.
- Each pull request includes a recovery plan.
- Every proposal, approval and deployment is logged.
From One Pipeline to the Whole Platform
Start with one pipeline. Expand after testing and review, with access limited to the work agreed.
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01
Assessment
Review your sources, data layers and failure history with Rudder Analytics. Choose the first pipeline and agree on success measures.
No access yet -
02
Design & Build
Set up read access to your code and data environment. Define the agent’s rules and test its proposals before rollout.
Read-only -
03
Rollout & Adoption
The agent proposes changes for the selected pipeline. Engineers test them outside production and approve the release.
Writes to a branch -
04
Run & Evolve
Extend coverage to more pipelines and cost reviews. Track accepted changes, recurring issues and the effect on maintenance work.
More pipelines, same controls
No in-house data engineers? Rudder Analytics engineers can review and approve on your behalf.
A Starting Point for Your Pipelines
Bring a pipeline that needs frequent attention. A strategy call identifies where AI can help and the engineering support required.
