Agentic physical AI data: a guide for robotics and autonomy data teams

Key takeaways

• •Who this is for. Data teams at autonomous driving, robotics and physical AI companies, and the physical AI data groups inside platform companies.
• •The problem today. Week-long searches through fleet logs, hand-written converters for every new robot and bad label batches reaching training.
• •What changes. Scenario attributes are indexed as logs land, so coverage questions take one query. Every label batch is gated on agreement and quality, so bad labels never reach a training set.
• •What stays with your people. Changes to released training sets and label specs: these shape model behavior, so the perception data lead approves.
• •The result. Researchers who get the training sets they ask for in days.

The PRD for agentic physical AI, robotics and perception data 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 physical AI, robotics and perception data teams

• •Put your data pipeline on autopilot. Scenario attributes are indexed as logs land, so coverage questions take one query.
• •Run the data back office without the busywork. The work that fills the week today moves to agents: week-long searches through fleet logs, hand-written converters for every new robot and bad label batches reaching training.
• •Scale data without scaling headcount. Every label batch is gated on agreement and quality and new sensor and robot formats are mapped to the standard, so the data you manage grows without the team having to.
• •Get the plumbing off your plate. Researchers who get the training sets they ask for in days.

Who this is for

Data teams at autonomous driving, robotics and physical AI companies, and the physical AI data groups inside platform companies. These teams sit between research, data engineering and labeling operations. Titles include Senior PM (Physical AI Robotics Data), Senior Manager of Product (Perception Data) and Autonomy Data Platform Lead.

The product you ship

• •Curated training sets for perception, planning and manipulation models.
• •Labeled data from internal and vendor labeling operations, with quality controls.
• •Scenario and coverage libraries that show what the fleet has and has not seen.
• •Data standards for formats, quality and distribution across teams.

Where the week goes

• •Fleet logs are enormous. Ingesting, indexing and finding the right minutes takes days.
• •Formats differ by sensor, robot and vendor. Every new platform means a new converter.
• •Labeling throughput is the bottleneck. Label specs change and vendor quality drifts.
• •Coverage is guesswork. Nobody can say quickly how many night-time left turns in rain the set holds.
• •Storage costs grow with every drive.

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 15% 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 physical AI, robotics and perception data teams:

• •Coverage question answered: from about 3 days to seconds
• •Bad label batch quarantined: from about 3 days to 8-15 min
• •Raw logs tiered: from about a month to hours

Across the routine work in this domain:

The biggest change is Bad label batch quarantined: from about 3 days to 8-15 min. Work that waited in queues finishes in minutes or hours, because agents detect, fix and verify in one pass.

Use cases, in depth

A night-time pedestrian label problem

Today. A vendor's agreement drops and bad labels reach training.

With Data Workers. Quality Monitoring catches it on the first batch, Incident Debugging ties it to a missed spec change, the batch is quarantined and one decision goes to the data lead.

What changes. Bad labels never reach a model.

Building a rare-scenario set

Today. Engineers spend a week searching for rain and night-time left turns.

With Data Workers. Autopilot builds the set as a mission from indexed fleet logs, with a coverage report.

What changes. Researchers get the data they asked for in days.

A new gripper platform

Today. Logs from the new platform cannot be used until someone writes a converter.

With Data Workers. Schema Evolution maps the format to the standard and Pipeline Building writes the conversion with tests.

What changes. New hardware produces training data from day one.

Agents working together: A night-time pedestrian label problem

A labeling vendor's agreement scores drop on pedestrians at night. Quality Monitoring catches it in the first batch, Incident Debugging ties it to a spec change the vendor missed, the batch is quarantined from the training set, and Conductor sends one decision to the perception data lead: relabel or exclude, with the cost of each.

Minute by minute

Illustrative example. A vendor's label batch arrives for night-time pedestrians.

The agents on the job

Context Wizard makes coverage questions answerable in plain English with the source logs attached. Conductor keeps labeling and curation pipelines on schedule and verified. Spellbook gives research a governed catalog of scenarios, datasets and labels.

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:

• •Scenario attributes are indexed as logs land, so coverage questions take one query.
• •Every label batch is gated on agreement and quality, so bad labels never reach a training set.
• •New sensor and robot formats are mapped to the standard, so new hardware produces usable data on day one.

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 released training sets and label specs always goes to a named approver: these shape model behavior, so the perception data lead approves.
• •Safety-critical release decisions: these stay with people.

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

Storage tiering closes autonomously 88% of the time. For Released training sets, autonomous closure is 0% by design: every change is proposed with a full diff and approved by a named person.

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: Fleet log storage; ROS or MCAP formats; labeling vendors; Spark or Ray; training pipelines. 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

Fleet data can include people and locations; safety programs require reproducible datasets. Data Workers versions every set with its sources 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 physical AI, robotics and perception 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. Labeling platforms label; Data Workers gates quality, standardizes formats and versions training sets.

The case for your CTO

What this replaces. Week-long searches through fleet logs, hand-written converters for every new robot and bad label batches reaching training.

What your team gets back. Researchers who get the training sets they ask for in days.

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 released training sets and label specs stay at L2 with a named approver; and one org-wide stop halts all autonomous work.

Why now. Fleet data grows faster than curation teams.

The first win. Label gating on one vendor. 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 physical AI, robotics and perception data 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 released training sets and label specs always go to a named approver.
• •Autonomous, under your governance. Routine work runs on its own: scenario attributes are indexed as logs land, every label batch is gated on agreement and quality, and new sensor and robot formats are mapped to the standard. Your team governs the agents instead of doing the upkeep.
• •The agentic enterprise. Researchers who get the training sets they ask for in days. People set direction, and the platform carries it out.

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

The pilot: how physical AI, robotics and perception data teams start

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

• •What we connect. Fleet log storage; ROS or MCAP formats; labeling vendors; Spark or Ray; training pipelines, over MCP, inside your infrastructure. Nothing migrates.
• •Scope. Label gating on one vendor.
• •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. Scenario attributes are indexed as logs land and every label batch is gated on agreement and quality start running on reversible work. Changes to released training sets and label specs stay at L2 with a named approver.
• •At day 30. We review the impact metrics against your baseline:

• •Coverage question answered: from about 3 days to seconds
• •Bad label batch quarantined: from about 3 days to 8-15 min
• •Raw logs tiered: from about a month to hours
• •the share of storage tiering 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 48% of agent actions run fully autonomously and manual work falls below 10%, while released training sets stay with a named approver.

FAQ

Does it support MCAP? Formats are mapped to your standard.

Can it answer coverage questions? Yes, from indexed attributes.

Who approves released sets? A named lead at L2.

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

• •NVIDIA, Senior PM Physical AI Robotics Data: https://jobs.anitab.org/companies/nvidia/jobs/86205790-senior-product-manager-physical-ai-robotics-data
• •NVIDIA, Senior Manager Product Perception Data: https://jobs.anitab.org/companies/nvidia/jobs/87315616-senior-manager-product-perception-data
• •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

Related: Best practices for agentic physical AI data · GenAI training data playbook