The agentic data platform for retail and e-commerce data teams
If you are a data leader at a retailer or retail media network, this is your playbook. It covers what your team ships, where its week goes today, the use cases agents take over first, the autonomy you can safely grant in each domain, and what to show leadership after the first quarter.
Key takeaways
- •Who this is for. Data leaders and teams at retailers, marketplaces and retail media networks.
- •The problem today. Most of the data team's week goes to supplier tie-outs, brand onboarding tickets and privacy reviews.
- •What changes. Specialised agents take that work over across the systems you already run: they catch bad data where it enters, fix it where it lives and verify the number before anyone reads it.
- •What stays with your people. Anything regulated or customer-facing waits for a named approver with the full diff, blast radius and rollback plan in front of them.
- •The result. Data teams that spend their time on new insights, audiences and measurement products.
The PRD for agentic data in retail
This is the requirements document we would write with you. Everything below expands on it.
| Who | Data leaders and teams at retailers, marketplaces and retail media networks. |
| The job | Transaction data that sells itself. |
| Environment | POS and e-commerce platforms; loyalty; Snowflake, BigQuery or Databricks; dbt; clean rooms; supplier portals, and the other platforms your teams run. |
| Constraints | Regulated and customer-facing numbers always go to a named approver; agents run in your infrastructure under the roles you already govern. |
| Success is measured by | Supplier question answered: from about 2 days to seconds; Brand onboarded to portal: from about 2 weeks to 10-15 min; Finding the right dataset: from about 3 hours to 30 s; catalog upkeep closed autonomously 88% of the time. |
| Owned by | The head of data or CDO, with domain owners as named approvers. |
| Pilot scope | Supplier tie-out or peak feeds at L2. |
What going fully agentic and autonomous means in retail
- •Put your data pipelines on autopilot. Agents catch bad data where it enters, fix it where it lives and verify the number before anyone reads it.
- •Run the data back office without the busywork. The week that goes to supplier tie-outs, brand onboarding tickets and privacy reviews moves to agents.
- •Scale data without scaling headcount. New sources, partners and data products are onboarded as missions, not projects.
- •Get the plumbing off your plate. Data teams spend their time on new insights, audiences and measurement products.
The shift
The largest retailers no longer just use their data. They sell it. Retail media networks, supplier insights portals and clean-room collaborations turn first-party transaction data into revenue, and suppliers pay for it the way they pay for any product.
That changes the bar. When data was internal, a wrong number cost a meeting. Now a wrong number in a supplier portal or a retail media report costs a renewal. Yet the data behind these products is still maintained like an internal warehouse: store-system changes break rollups, hierarchies drift, supplier onboarding is a ticket queue and every report is tied out by hand.
Our view: retail data products should be run like the products they are. Agents keep POS, e-commerce, loyalty and media data landing and tied out, onboard suppliers as a mission, enforce privacy thresholds in the pipeline and verify every supplier-facing number before it publishes.
How data work in retail got here
The merchandising warehouse era. Sales data served buyers and finance. Suppliers got scorecards through vendor portals built on the same warehouse.
The retail media era. Loyalty data became audiences and closed-loop measurement. Retailers built data science units, media networks and clean rooms, and data teams became product teams with external customers.
The agentic back office. Agents run the upkeep behind those products, so the team can focus on new insights modules, audiences and measurement.
The three eras side by side:
Each era changed the tools. Only the agentic back office changes who does the work.
A day on a retail data team today
A store-system upgrade in 300 stores starts sending a new tender type. Sales for those stores drop 4% in the warehouse. The supplier insights portal refreshes at 6am, and three large brands email their account teams by 10am asking why their share fell. The data team spends two days proving it was a data issue, not a market one.
That morning is typical, not exceptional. Across a week, this is where the team's time goes, today and with agents on the back office:
Time spent on new data products moves from 18% of the week to 52%. Most of the rest becomes review: people steer and approve agent work instead of doing it by hand.
Then and now
| Work | Today | With Data Workers |
|---|---|---|
| Store-system and POS changes | Found when supplier numbers move | Caught on arrival, mapped, tied to finance before publishing |
| Item and store hierarchy changes | Broken rollups after every reorg | Versioned hierarchies with prior periods restated |
| Supplier onboarding | A ticket per brand | A mission: access, templates and documentation per brand |
| Supplier-facing numbers | Tied out by hand when someone complains | Tied to finance and media every night, held if they do not tie |
| Privacy thresholds | Checked in reviews | Enforced in the pipeline before anything publishes |
| Transaction-table cost | Grows every week | Duplicates and stale copies retired continuously |
These are the impact metrics we move:
- •Supplier question answered: from about 2 days to seconds
- •Brand onboarded to portal: from about 2 weeks to 10-15 min
- •Finding the right dataset: from about 3 hours to 30 s
Across the routine work:
The biggest change is Brand onboarded to portal: from about 2 weeks to 10-15 min. Work that waited in queues finishes in minutes or hours, because agents detect, fix and verify in one pass.
Use cases, in depth
1. The tender-type change that looked like a share drop
Today. Suppliers notice first. The team spends days proving it was data.
With Data Workers. Ingestion flags the unfamiliar tender code on the first load, Schema Evolution maps it, Pipeline Building updates the sales model, Quality Monitoring confirms store sales tie to finance, and Conductor holds the supplier-portal refresh for one approval.
What changes. Suppliers never see the dip.
2. Onboarding 40 brands to the insights portal
Today. Each brand needs scoped access, a clean-room template and onboarding documentation. Account teams wait on the data team.
With Data Workers. Supplier onboarding runs as an Autopilot mission. Access & Governance scopes each brand under your policies, clean-room templates are created per brand, and Spellbook pages document what each brand can see.
What changes. Commercial teams sign brands as fast as they can sell.
3. Closed-loop measurement advertisers believe
Today. Retail media reports and the insights portal compute sales and lift differently. Brands ask why.
With Data Workers. Data Context Wizard holds governed definitions of a household, a sale, an incremental sale and a lift, used by every product you sell. Quality Monitoring ties media-attributed sales to the sales of record every night.
What changes. One number, everywhere a brand looks.
4. Launching a new insights module
Today. A new module, such as pre-purchase browse behavior, is a multi-quarter build.
With Data Workers. The module is built as a mission: clickstream sources landed, modeled, aggregated under privacy thresholds, tested and documented by agents. People decide what the module shows and how it is priced.
What changes. New data products reach suppliers in weeks.
Agents working together: A store-system change that looked like a share drop
The tender-code change from the first use case, end to end. Five agents and one approver keep a data change from looking like a market change to suppliers.
Minute by minute
Illustrative example. POS loads land nightly in BigQuery; the supplier portal refreshes at 06:00. Supplier-facing numbers are at L2.
| Time | Who | What happens |
|---|---|---|
| 02:10 | POS | 300 stores send a new tender code |
| 02:12 | Schema Evolution agent | Unfamiliar value flagged; detect_schema_change |
| 02:30 | Pipeline Building agent | Sales model diff with tests |
| 02:41 | Quality Monitoring agent | run_quality_check: store sales tie to finance |
| 05:20 | Spellbook Data Catalog | Insights lead approves the refresh |
| 06:00 | Supplier portal | Brands see correct sales |
How it fits your stack
Data Workers connects to what you already run: POS and e-commerce platforms; loyalty; Snowflake, BigQuery or Databricks; dbt; clean rooms; supplier portals. Each agent is an MCP server, so the same agents work inside Claude Code, Cursor or Codex and in Slack or Teams through Spellbook. Nothing migrates.
- •Runs in your infrastructure with your credentials and your model key. The hosted Autonomous Data-Conductor sees workflow metadata only, never rows.
- •Uses the access you already govern. Agents act under scoped service identities and your existing roles; every grant goes through provision_access and is logged.
- •Proposes changes as diffs in your dbt project, orchestrator and warehouse, with blast radius, tests and a rollback point, and applies them only at the autonomy level you set for that domain.
- •Open-source core. The Apache-2.0 community edition ships 11 agents and 160+ MCP tools, so your engineers can read exactly what the agents call.
How autonomy expands over the first year
Autonomy is earned per task class. In a mature deployment, it looks like this:
Catalog upkeep closes autonomously 88% of the time. For Supplier-facing numbers, autonomous closure is 0% by design: every change is proposed with a full diff and approved by a named person.
A typical path to get there:
- •Month 1, L1. Agents map sources, hierarchies, supplier products and definitions.
- •Months 2 to 3. Catalog upkeep and cost fixes at L4. Hierarchy remaps in staging, reruns and quality rules at L3.
- •Months 4 to 6. Supplier onboarding and new insights modules as missions.
- •Months 6 to 12. Remap classes with a clean record move up. Supplier-facing numbers and new supplier access stay at L2. Commercial terms stay with people.
The same path as a curve, month by month:
By month 12, more than 48% of agent actions run fully autonomously and manual work falls below 10%, while supplier-facing numbers stay with a named approver.
What happens when an agent is wrong
- •At L2 propose, nothing changes until a named person approves. The proposal shows the full diff, the blast radius and how to roll it back. Unanswered requests expire and escalate.
- •At L3 act, reversibly, every change keeps a rollback point, and Conductor verifies the downstream number after the change. If verification fails, the change is reversed and the domain drops a level.
- •No agent approves or promotes its own work. Autonomy only grows on evidence, per repair class, and repeated errors shrink scope automatically.
- •One org-wide stop halts all autonomous dispatch, and every action lands in a hash-chained audit trail you can export with generate_audit_report.
Governance and regulation
Retailers handle card data under PCI DSS and consumer data under CCPA and GDPR. Data Workers traces classified card data to every copy, keeps opt-out tables fresh, enforces aggregation thresholds and logs every change.
Data Workers doesn't make you compliant; your controls, people and assessors do. It supplies controls and evidence wherever agents touch data. Not legal advice.
The alternatives retail data teams weigh
Build it yourselves. A strong team can wire a coding agent to MCP servers for the warehouse and dbt. The work is everything around it: routing approvals to the right owner, blast radius, rollback, receipts, and current context about every other system.
Observability, catalogs and point tools. Observability alerts, catalogs document, and quality tools test. Data Workers takes their signals, diagnoses, proposes the fix, verifies it and leaves the receipt.
Platform-native agents. Snowflake made its managed MCP server and Cortex Agents generally available on Nov 4, 2025, and Databricks made Genie One generally available on Jun 16, 2026. They are excellent inside their platform; this work crosses the source systems, orchestrator, warehouse and BI, and Data Workers works across all of them.
In this space specifically. Commerce platforms, CDPs and retail media tools each own a slice; Data Workers keeps the data across them correct.
The case for your CTO
What this replaces. The week spent on supplier tie-outs, brand onboarding tickets and privacy reviews.
What your teams get back. Time for new insights, audiences and measurement products, on data that is verified before anyone reads it.
Integration effort. Connecting is configuration, not a migration. The agents attach over MCP to the platforms you already run, under the roles you already govern, inside your own infrastructure.
Risk and control. Autonomy is set per domain, starts at L1 and rises only on evidence. Regulated and customer-facing numbers stay at L2 with a named approver, and one org-wide stop halts all autonomous work.
Why now. Retail media and AI personalization raise the stakes.
The first win. Supplier tie-out or peak feeds at L2. The pilot below lays out the scope, what you see each week and the cost.
The climb: from assisted to autonomous to agentic
Our thesis is that the data platform climbs the way analytics did, in stages. In retail, that climb looks like this:
- •Assisted. Agents work alongside each data team, read the whole stack and propose every fix with a receipt. Regulated and customer-facing numbers always go to a named approver.
- •Autonomous, under your governance. The upkeep behind supplier tie-outs, brand onboarding tickets and privacy reviews runs on its own, domain by domain, as the receipts earn it.
- •The agentic enterprise. The platform largely runs itself. People set direction and standards, and the teams spend their time on new insights, audiences and measurement products.
You choose the altitude, every step is visible and reversible, and the pilot below is the first rung.
The pilot: how retail data teams start
The pilot is how a team proves the value on its own data before committing.
- •What we connect. POS and e-commerce platforms; loyalty; Snowflake, BigQuery or Databricks; dbt; clean rooms; supplier portals, over MCP, inside your infrastructure. Nothing migrates.
- •Scope. Supplier tie-out or peak feeds at L2.
- •First week. Agents run at L1 and show the fixes they would have made, with receipts.
- •By week two. Reversible upkeep (reruns, staging fixes, documentation, cleanup) runs on its own; regulated and customer-facing numbers stay at L2 with a named approver.
- •At day 30. We review the impact metrics against your baseline:
- •Supplier question answered: from about 2 days to seconds
- •Brand onboarded to portal: from about 2 weeks to 10-15 min
- •Finding the right dataset: from about 3 hours to 30 s
- •the share of catalog upkeep closed autonomously, targeting 88%
- •What you decide. Which domain or team goes next. The pilot fee is credited in full against the first year.
- •Cost. Start with a pilot ($7,500 one-time, credited in full against the first year). Scale is from $1,000 a month and Enterprise from $3,000 a month, billed annually, with unlimited seats, no usage meter and no markup on model spend. See pricing.
Teams we work with in retail
FAQ
Shopify or Commerce Cloud? Yes, through the warehouse and APIs.
PCI scope? Your assessor decides; agents read column names, not values, in review.
Opt-outs? Fresh before every sync.
Where do the agents run? In your infrastructure, with your credentials and model key. The hosted Conductor sees workflow metadata only.
Do we have to migrate anything? No. Your warehouse, dbt project, orchestrator, catalog and BI stay where they are.
Sources
- •Data Workers, retail and e-commerce page: https://dataworkers.io/blog/agentic-data-platform-for-retail-and-e-commerce/
- •Data Workers pricing: https://dataworkers.io/pricing/
- •Data Workers open-source repository (tool registrations): https://github.com/DataWorkersProject/dataworkers-claw-community
- •Snowflake, managed MCP server and Cortex Agents GA release note (Nov 4, 2025): https://docs.snowflake.com/en/release-notes/2025/other/2025-11-04-cortex-agents-mcp
- •Databricks, Genie One launch (Jun 16, 2026): https://www.databricks.com/company/newsroom/press-releases/databricks-launches-genie-one-all-new-agentic-coworker-every-team