A playbook for the Head of Manufacturing Data

Key takeaways

• •Who this is for. The team that owns the plant data backbone: MES, historians, SCADA and PLC data, batch records and genealogy, and their integration with ERP and quality systems.
• •The problem today. Weeks of interface work per new line, unit and timestamp mismatches and genealogy gaps during quality holds.

• •What changes. Tags are mapped to the plant entity model as they appear, so new lines report on day one. Units, timestamps and duplicates are enforced on every load, so analytics run on clean data.
• •What stays with your people. Changes to batch and quality records: they can be regulated, so the plant data lead approves.

• •The result. Plant data engineers trust, from day one of every line.

The PRD for agentic plant data and MES teams

This is the requirements document we would write with you for your team. Everything below expands on it.

What going fully agentic and autonomous means for plant data and MES teams

• •Put your data pipeline on autopilot. Tags are mapped to the plant entity model as they appear, so new lines report on day one.
• •Run the data back office without the busywork. The work that fills the week today moves to agents: weeks of interface work per new line, unit and timestamp mismatches and genealogy gaps during quality holds.

• •Scale data without scaling headcount. Units, timestamps and duplicates are enforced on every load and genealogy stays complete, so the data you manage grows without the team having to.
• •Get the plumbing off your plate. Plant data engineers trust, from day one of every line.

Who this is for

The team that owns the plant data backbone: MES, historians, SCADA and PLC data, batch records and genealogy, and their integration with ERP and quality systems. It defines entity models for materials, batches, equipment and test results, and serves production, quality, process engineering and maintenance.

The shift

Plants generate more data than ever, but tags, batches and work orders rarely line up with business records. Every new line, site or platform migration multiplies the integration work. Agents can contextualize plant data, enforce schema, unit and timestamp discipline and keep genealogy complete, so engineers analyze instead of reconciling.

The product you ship

• •Contextualized plant data: tags mapped to equipment, batches and materials
• •Material and batch genealogy

• •Integrated MES, historian and ERP records
• •Reports and analytics for process engineering, quality and maintenance

Where the week goes

• •Historian tags lack context.
• •Timestamps and units disagree across systems.

• •Batch records have gaps and duplicates.
• •New lines and sites add interfaces.

• •Platform migrations threaten continuity.

Add it up and upkeep is most of the team's week. Here is how a typical week splits today, and once agents own the back office:

Time spent on new data products moves from 14% of the week to 50%. Most of the rest becomes review: people steer and approve agent work instead of doing it by hand.

Then and now

These are the impact metrics we move for plant data and MES teams:

• •Lot traced end to end: from about a week to 15-20 min
• •Unit mismatch fixed: from about 3 days to 15-20 min

• •Interface documented: from about a week to ~1 h

Across the routine work in this domain:

The biggest change is Lot traced end to end: from about a week to 15-20 min.

Use cases, in depth

A new production line

Today. Integrating a new line's tags, batches and MES events takes weeks of interface work.

With Data Workers. Connectors land historian and MES data, Schema Evolution maps tags to the equipment model, Quality Monitoring checks units and timestamps.

What changes. The line reports on day one.

Genealogy for a quality hold

Today. A quality hold needs a lot traced across materials, batches and equipment.

With Data Workers. Data Context Wizard keeps genealogy complete and traceable in one question.

What changes. Holds are scoped in minutes.

An MES platform migration

Today. Moving to a new MES risks losing history and interfaces.

With Data Workers. Data Migration moves interfaces and history in waves with parity checks.

What changes. No gap in plant data.

Agents working together: A new production line

Minute by minute

Illustrative example. Historian and MES data stream into a lakehouse; production reports refresh every shift.

The agents on the job

Data Context Wizard keeps one governed graph of definitions, lineage and owners that every agent and person answers from. Autonomous Data-Conductor runs each job from detection to a verified result. Spellbook Data Catalog is where your team approves, audits and rolls back, and asks questions in plain English.

What goes on autopilot

Once a domain has earned it, this work runs on its own, and each item removes a problem your team deals with today:

• •Tags are mapped to the plant entity model as they appear, so new lines report on day one.
• •Units, timestamps and duplicates are enforced on every load, so analytics run on clean data.

• •Genealogy stays complete, so quality holds are scoped in minutes.

Where your people stay in charge

Agents never get blanket access. Each domain has its own autonomy level, set by you, and it only rises when the receipts show the agents are reliable. Everything starts at L1, observing only; reversible upkeep such as reruns, staging fixes, documentation and cleanup is usually first to run on its own.

• •Changes to batch and quality records always goes to a named approver: they can be regulated, so the plant data lead approves.
• •Production and release decisions: these stay with people.

Once a deployment has matured, here is how that ladder plays out across task classes:

Interface catalog upkeep closes autonomously 88% of the time. For Batch and quality records, autonomous closure is 0% by design.

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.

How it fits your stack

Data Workers connects to what you already run: Historians (PI, Canary), SCADA and PLC data, MES platforms, ERP (SAP, Oracle), quality systems; Databricks or Snowflake; Kafka. 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.

Governance and regulation

Batch records and quality data can be regulated (GMP in pharma and food). Data Workers keeps lineage and holds changes to batch and quality records for a named approver.

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 plant data and MES 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. MES and historian platforms record plant data; Data Workers contextualizes and verifies it across systems, independent of any one vendor.

The case for your CTO

What this replaces. Weeks of interface work per new line, unit and timestamp mismatches and genealogy gaps during quality holds.

What your team gets back. Plant data engineers trust, from day one of every line.

Integration effort. Connecting is configuration, not a migration. The agents attach over MCP to the warehouse, orchestrator, dbt project and source systems you already run, under the roles you already govern, inside your own infrastructure. Your engineers can read every tool the agents call in the open-source core.

Risk and control. Autonomy is set per domain. Everything starts at L1, observing only; reversible work moves up only when the receipts justify it; changes to batch and quality records stay at L2 with a named approver; and one org-wide stop halts all autonomous work.

Why now. New lines, sites and platform migrations keep multiplying integration work.

The first win. Contextualization for one line. 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, and that the hard part of data work was never knowing what to do but doing it. For plant data and MES teams, the climb looks like this:

• •Assisted. Agents work alongside your team. They read the whole stack, show what needs doing and propose every fix with a receipt; changes to batch and quality records always go to a named approver.
• •Autonomous, under your governance. Routine work runs on its own: tags are mapped to the plant entity model as they appear, units, timestamps and duplicates are enforced on every load, and genealogy stays complete. Your team governs the agents instead of doing the upkeep.

• •The agentic enterprise. Manufacturers will run every plant on one owned data model, with lines and sites onboarded in days and platform choices free of vendor lock-in.

You choose the altitude, every step is visible and reversible, and the pilot below is the first rung.

The pilot: how plant data and MES teams start

The pilot is how your team proves the value on its own data before committing.

• •What we connect. Historians (PI, Canary), SCADA and PLC data, MES platforms, ERP (SAP, Oracle), quality systems; Databricks or Snowflake; Kafka, over MCP, inside your infrastructure. Nothing migrates.
• •Scope. Contextualization for one line.

• •First week. Agents run at L1 across the scoped domain. They map every pipeline, definition and lineage path and show the fixes they would have made, with receipts, so your team sees the value before anything changes.
• •By week two. Tags are mapped to the plant entity model as they appear and units, timestamps and duplicates are enforced on every load start running on reversible work. Changes to batch and quality records stay at L2 with a named approver.

• •At day 30. We review the impact metrics against your baseline:

- Lot traced end to end: from about a week to 15-20 min

- Unit mismatch fixed: from about 3 days to 15-20 min

- Interface documented: from about a week to ~1 h

- the share of interface catalog upkeep closed autonomously, targeting 88% as the deployment matures

• •What you decide. Whether to expand to the next domain. 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.

The pilot is the start of a longer curve:

By month 12, more than 44% of agent actions run fully autonomously while batch and quality records stay with a named approver.

FAQ

Does it replace our MES? No; it contextualizes and verifies data across MES, historian and ERP.

Does it lock us to a vendor? No; the entity model is yours.

Can it help with GMP? It keeps lineage and approvals on regulated records.

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 pricing: https://dataworkers.io/pricing/

• •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