Location, vehicle and network data that keeps moving: the agentic data back office for mobility, fleet and logistics companies

Key takeaways

• •Who this is for. Data leaders and teams at mobility, fleet, telematics and logistics companies.
• •The problem today. Most of the data team's week goes to zone outages, score investigations and stale ETAs.
• •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 pricing, insurance and visibility products.

The PRD for agentic data in mobility and transportation

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

What going fully agentic and autonomous means in mobility and transportation

• •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 zone outages, score investigations and stale ETAs 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 pricing, insurance and visibility products.

The shift

Mobility businesses price, dispatch and insure on data that is minutes old. Geofences change, firmware changes, partners change formats, and consent rules now apply to driving data. The cost of a wrong number is immediate: a mispriced trip, a stale ETA, a score an insurer prices on.

Our view: agents should keep location, vehicle and network data correct at the speed it changes, with people approving anything that moves a price or a score.

A day on a mobility and transportation data team today

An ops edit leaves a gap between zones and trips price wrong all evening. An OEM firmware update shifts driving scores for one model year. A hub's scanners drop events and ETAs go stale for 80,000 packages.

That week is typical, not exceptional. Here is where the time goes, today and with agents on the back office:

Then and now

These are the impact metrics we move:

• •Map update verified: from about 2 days to ~1 h
• •Geofence gap fixed: from about 4 hours to 8-15 min
• •Stale feature rerun: from about 2 hours to minutes

Across the routine work:

Use cases, in depth

Zones and pricing features that never fall through

Today. An ops edit leaves a gap between zones; trips in the gap get no pricing.

With Data Workers. Quality Monitoring flags unassigned trips, Incident Debugging traces the edit, Conductor holds the zone change at L2.

What changes. No trips fall through. Read the playbook.

Scores protected from firmware artifacts

Today. A model year's hard-braking rate jumps and insurers ask why.

With Data Workers. Streaming flags the payload, Quality Monitoring isolates the model year, Incident Debugging ties it to firmware, Pipeline Building normalizes sampling, and the score refresh waits for the actuary.

What changes. Partners never price on a firmware artifact. Read the playbook.

ETAs verified per hub

Today. A hub's scanners drop events for two hours; ETAs for 80,000 packages go stale.

With Data Workers. Quality Monitoring flags the per-hub volume drop, Incident Debugging traces it to an upload failure, Conductor reruns and verifies ETAs.

What changes. Shippers see correct ETAs. Read the playbook.

Agents working together: A geofence gap that dropped surge pricing

Minute by minute

Illustrative example. Zones live in a geo service; trips stream into a lakehouse; pricing reads features every minute.

How it fits your stack

Data Workers connects to what you already run: Kafka; H3 or S2 indexing; Databricks or BigQuery; feature stores; maps and routing services. 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:

A typical path: month 1 at L1 everywhere; months 2 to 3, L3 and L4 on reversible upkeep; months 4 to 6, new data products built by the swarm; months 6 to 12, wider autonomy where the evidence supports it, with regulated or customer-facing numbers kept at L2.

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

Location and driving data are personal data, and regulators now expect affirmative consent for sharing driving data. Data Workers enforces consent and access policies and logs every delivery.

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 mobility and transportation 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. Telematics, maps and visibility platforms each own a slice; Data Workers keeps the data across them correct.

The case for your CTO

What this replaces. The week spent on zone outages, score investigations and stale ETAs.

What your teams get back. Time for pricing, insurance and visibility 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. Consent rules and real-time expectations raised the bar.

The first win. Zone, firmware or hub checks on one market 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 mobility and transportation, 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 zone outages, score investigations and stale ETAs 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 pricing, insurance and visibility products.

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

The pilot: how mobility and transportation data teams start

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

• •What we connect. Kafka; H3 or S2 indexing; Databricks or BigQuery; feature stores; maps and routing services, over MCP, inside your infrastructure. Nothing migrates.
• •Scope. Zone, firmware or hub checks on one market 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:

• •Map update verified: from about 2 days to ~1 h
• •Geofence gap fixed: from about 4 hours to 8-15 min
• •Stale feature rerun: from about 2 hours to minutes
• •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 mobility and transportation

• •Marketplace and geospatial data teams
• •Telematics and connected-vehicle data teams

FAQ

Does it work in real time? Yes, with streaming checks.

Can it enforce consent? Every delivery records consent basis.

Who approves price or score changes? A named owner 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

• •Uber Engineering, H3: https://www.uber.com/blog/visualizing-city-cores-with-h3/
• •The Register, FTC order on GM tracking: https://www.theregister.com/2026/01/15/ftc_gm_tracking_ban/
• •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