Does this replace our orchestrator or data quality tool?

No. Agents read from the schedulers, warehouses and checks you already run. They investigate what those tools report and fill the gaps between them.

Can agents touch production data?

Only where your policy allows it. Most teams start read-only, then let agents rerun safe jobs alone and keep backfills behind an approval.

How do agents know who owns a table?

From the tags, catalog entries and repository owners you already have. Gaps are flagged so your team can fill them once.

What about failures that do not raise an alert?

Agents check freshness, row counts and null rates against history, so a load that succeeds with bad data still gets investigated.

AWS Partner

SOC 2 Reports

Frontier Investigationfor Data Pipelines

Failed and late pipelines traced to the bad source, schema or job behind them.

Connect your warehouse, your streams and the jobs between them. Agents pick up every failed, late or low-quality run, trace it upstream to the source, schema change or job that broke it, and hand the owning team a fix and a backfill plan before the morning dashboards load.

Book a Demo See how it works

AWS Glue job alertcause found

orders_daily load failed, 06:00 dashboards at risk

Failure: Job orders_to_redshift failed on 3 retries since 02:40
Upstream: Source table orders added a nullable discount_code column
Cause: Schema change broke the COPY mapping in the load step
Impact: 4 dashboards and 1 finance export stale
Fix: Update the column mapping, rerun, backfill 1 partition

Investigated in 2m 30s. Waiting for data engineer approval

[The work behind every broken pipeline]

Pipelines fail at night.The business finds out at 9am.

01The manual work

Trace

A job fails or a table goes stale. A data engineer reads job logs, walks the lineage upstream and diffs schemas until the cause turns up.

02The agent handoff

Cause found

Frontier agents follow the failure upstream, find the source change or bad batch behind it, and prepare the fix and the backfill.

03Your engineers’ role

Approve

Set which reruns agents may start alone. Review the fix and the backfill and decide what touches production data.

[Where CloudThinker fits]

Your stack stays.Agents work inside it.

01Pipelines

Already in your data stack

AWS Glue and Amazon RedshiftJobs, loads and warehouse
Azure Data Factory and SynapsePipelines and warehouse
Google BigQuery and DataflowJobs and datasets
KafkaStreams feeding the loads
Snowflake and DatabricksWarehouse and lakehouse jobs

No change to your pipelines

02Sources

Your system of record

PostgreSQLOperational source tables
MySQLApplication databases
MongoDBDocument sources
GitHubPipeline and schema code

Source of truth stays put

03Investigation

CloudThinker

Check the runFreshness, row counts and nulls
Find the breakThe source, schema or job behind it
ProveEach finding linked to a query or log
ProposeA fix and a safe backfill plan

Read-only by default

04Response

Fresh data, not tickets

SlackRoot cause posted to the data channel
PagerDutyPage only when a load is at risk
JiraTicket for the source owner
GitHubPull request with the fix

Backfill runs on approval

Logos show common stacks. CloudThinker connects to each one through read-only access you approve.

[Example scenario]

Wednesday, 02:40. The nightly load fails.Finance opens the dashboard at 08:00.

A 12-person data team feeding Amazon Redshift from PostgreSQL and Kafka through AWS Glue. 40 dashboards depend on the overnight loads.

02:40
Job failsSignal
AWS Glue: orders_to_redshift failed. Retries at 02:50 and 03:00 fail the same way.
03:01
Agent picks it upAgent
Reads the job logs in CloudWatch, the load script and the source schema history, then walks the lineage downstream.
03:03
Cause, with proofAgent
A release at 18:20 added discount_code to the orders table. The COPY mapping still expects the old column order.
03:04
Fix and backfill proposedAgent
Merge request updates the mapping. Plan reruns the job and backfills one partition. Impact: 4 dashboards, 1 finance export.
07:10
Data engineer approvesYour team
Reads the diff over coffee, approves the merge request and the backfill.
07:32
Data verifiedAgent
Load complete. Row counts match the source within 0.01%. Dashboards refreshed before finance logs in.

#data-incidents4 messages

AWS Glue02:40
Job run failed: orders_to_redshift (attempt 1 of 3).
CloudThinker03:04
Cause: orders gained discount_code at 18:20 (release 4.12). The COPY mapping in load_orders.py is now off by one column. Fix: MR !219. Backfill: partition 2026-10-07. 4 dashboards stale until then.
Data engineer07:10
Diff looks right. Approved both.
CloudThinker07:32
Load complete. 1,284,551 rows, matching source within 0.01%. Dashboards refreshed. DATA-377 opened to add a schema contract check.

from failed retry to known cause: 4 min
approval from the data engineer: 1
stale dashboards at 08:00 in this example: 0

An illustrative example. Team, systems and times are representative, not a specific customer.

[Frontier investigation agents]

Every failed run gets traced.Every stale table gets an owner.

Agents read jobs, schemas and lineage together, so a failed load arrives with its upstream cause, and a quiet data quality drop gets caught before a report ships.

Trace it upstream: Job logs, schema history and source releases lined up against the run that failed.
Catch bad data: Row counts, null rates and freshness checked against history, so silent failures surface too.
Know what is late: Each table mapped to the dashboards, exports and teams that depend on it, with the time they need it.
Hand over a fix: Code fixes, reruns and backfills prepared and run only at the approval level you set.

[What changes]

Same team. Same tools.Far less of the work by hand.

Moment	Today	With frontier agents
A failed overnight run	Found when someone opens a dashboard	Investigated minutes after the last retry
Finding the cause	Logs, lineage and schemas checked by hand	Traced upstream with the evidence attached
Silent quality drops	Noticed by a business user	Caught by checks against history
Backfills	Written and run from memory	Planned, reviewed and verified against the source
Telling the business	A message after the meeting started	Owners and impact known before 08:00

[Integrations]

Connects to the rest of your stack.Read-only to start.

Amazon Redshift
AWS Glue
Amazon S3
Snowflake
Databricks
BigQuery
Kafka
PostgreSQL
MySQL
MongoDB
Elasticsearch
Amazon CloudWatch
Datadog
PagerDuty
Slack

[Adoption path]

One pilot.Then company-wide.

The rollout follows the four phases of the AWS Cloud Adoption Framework, so it fits the plan your cloud team already runs.

01Envision
Pick one critical load
Connect the job, its source and its target read-only. Let agents investigate failures in shadow mode next to your team.
02Align
Agree what agents may rerun
Decide which reruns are safe alone, which backfills need approval, and who owns each table.
03Launch
Roll out domain by domain
Add each data domain’s pipelines on the same policies, report format and audit trail.
04Scale
Make it the default
New pipelines launch with investigation and quality checks on. Findings feed schema contracts and runbooks.

[AWS guidance]

Built the way AWS says to run it.Reviews start from a known reference.

[Trust and control]

Agents do the work.Your team keeps control.

You approve every change: Agents propose. Nothing touches production until someone on your team says yes, and you set that rule per system.
Every action on the record: Each step is logged, attributed and reversible, ready for your auditors.
Certified for enterprise: SOC 2 Type II and ISO 42001, with reports in our trust center.
Runs where you need it: In our cloud, through AWS Marketplace, or inside your own account.

[Questions]

What teams askbefore they start.

Does this replace our orchestrator or data quality tool?: No. Agents read from the schedulers, warehouses and checks you already run. They investigate what those tools report and fill the gaps between them.
Can agents touch production data?: Only where your policy allows it. Most teams start read-only, then let agents rerun safe jobs alone and keep backfills behind an approval.
How do agents know who owns a table?: From the tags, catalog entries and repository owners you already have. Gaps are flagged so your team can fill them once.
What about failures that do not raise an alert?: Agents check freshness, row counts and null rates against history, so a load that succeeds with bad data still gets investigated.

[Go deeper]