You're on Coalesce Quality (formerly SYNQ): It Watches Your Data Products. Data Workers Fixes What Breaks Them
Coalesce Quality monitors your data products and Scout investigates. Data Workers diagnoses, fixes and verifies the incident across your stack, with approval.
Your team builds pipelines in Coalesce Transform on Snowflake, and since March 2026 the quality layer sits in the same platform: Coalesce acquired SYNQ and relaunched it as Coalesce Quality. With Coalesce 2.0 (Sep 15, 2026), Transform, Catalog and Quality run on one context layer, and Quality's built-in tests can circuit-break a Transform job. You define data products such as "Revenue reporting", give each an owner, and attach tests, contracts, SLOs and monitors. When something fails, issues group into an incident with lineage and the owner attached, the alert lands in Slack, and Scout, the Coalesce Quality agent, triages it and proposes a fix in code. Coalesce Quality is the smoke alarm on your data products, and a well-placed one. Data Workers is the crew: it diagnoses, fixes and verifies the incident behind approvals across the whole estate, from the source system that changed to the dashboard, with a receipt for every change.
Key takeaways
- •Coalesce Quality keeps its job. Data products, owners, tests, monitors, SLAs, incidents and Scout stay where your team set them up.
- •Scout's fix lives in the code Coalesce runs. Data Workers checks that change against the rest of the estate and the definitions finance approved, and carries the repair through every system it touches.
- •One approval, one receipt. A named owner approves the whole repair in Spellbook. Data Workers queues the rerun through Airflow, verifies the result and records who approved what and how to undo it.
- •Tiered MCP, per-domain autonomy. Coalesce Quality's MCP server separates Read-only, Write, Governance and Deploy at sign-in. Data Workers adds autonomy per domain, from L0 manual to L4 autonomous, for changes to the data itself.
Coalesce Quality is the smoke alarm on your data products. Data Workers is the crew.
A Friday at a B2B software company: Coalesce Transform on Snowflake, Salesforce replicated by Stitch, Airflow scheduling the Coalesce jobs, and an Omni dashboard the CFO reads at 9 a.m. An illustration, not a customer case.
| Time | System | What happens |
|---|---|---|
| Thu 17:20 | Salesforce | An admin adds the Opportunity Type value "Renewal Multi-Year" and bulk-moves 1,240 open multi-year renewals to it |
| Fri 01:00 | Stitch | Replication loads the changed Opportunity rows into RAW_SALESFORCE.OPPORTUNITY in Snowflake |
| 02:00 | Airflow | The revenue_nightly DAG starts the Coalesce Transform job through Coalesce's API |
| 02:14 | Coalesce Transform | The STG_OPPORTUNITY node's CASE has no branch for the new value; the not-null test on ARR_MOTION fails and the job, set to stop on a failed test, halts before FCT_ARR |
| 07:00 | Coalesce Quality | The freshness SLA on the "Revenue reporting" data product breaches; the issue on FCT_ARR groups with the failed test and routes to the revenue analytics owner, who declares an incident from Slack |
| 07:06 | Scout | Root cause points at the CASE in STG_OPPORTUNITY; Scout drafts a node change that maps "Renewal Multi-Year" to Renewal |
| 07:09 | Data Workers | Reads the incident over Coalesce Quality's MCP server, traces lineage past the node to Stitch and the Salesforce picklist change, and maps the blast radius: FCT_ARR, FCT_PIPELINE, the sales commission extract and the Omni "ARR by segment" dashboard |
| 07:15 | Data Workers | Checks the draft against the ARR definition finance approved: multi-year renewals count at annualized value, so a straight mapping to Renewal would book the full contract amount for 1,240 deals |
| 07:22 | Data Workers | Proposes the repair in Spellbook: a node diff that maps the new value and annualizes multi-year amounts, an accepted-values test on Opportunity Type, a rerun from STG_OPPORTUNITY, and a note to the Salesforce admin owner |
| 07:50 | Spellbook | The revenue analytics owner reviews the diff, the definition it follows and the blast radius, approves, and merges the node change in Coalesce Transform |
| 08:05 | Airflow | Data Workers queues the DAG rerun; the Coalesce job runs from STG_OPPORTUNITY and every test passes |
| 08:30 | Snowflake | Data Workers verifies load lag on FCT_ARR against its baseline, row volume against last Friday and ARR totals against annualized Salesforce amounts |
| 08:35 | Coalesce Quality | The data product is fresh again; the on-call's client posts the receipt link as a comment on the incident and the owner closes it |
Coalesce Quality did its job well: the test stopped bad rows at 02:14, the SLA made the miss visible before anyone opened the dashboard, and Scout had a sensible draft by 07:06. What changed is everything around that draft. The cause was traced to a Salesforce change, the ARR rule that made the obvious fix wrong was in the proposal, a named owner approved one change set, and the CFO's 9 a.m. dashboard showed correct ARR.
| Job | What Coalesce Quality does | What Data Workers does |
|---|---|---|
| Detection | Tests, contracts, SLOs and anomaly monitors on nodes, columns and data products | Watches freshness, volume and schema changes across every platform, including sources outside the Coalesce project |
| Ownership and routing | Data products carry owners, priorities and alert routing; issues group into incidents | Joins the incident to history with get_incident_history, so a repeat break arrives with the last fix attached |
| Root cause | Scout analyzes lineage, run history and test results inside the Coalesce estate | Traces past Snowflake into ingestion and the source system, and checks the fix against governed business definitions |
| The fix | Scout drafts ready-to-ship code suggestions and deploys test recommendations as pull requests | Proposes the complete repair: the node diff, the guard test, the rerun plan and the upstream change for its owner, with blast radius |
| Running it | Your team reviews and deploys the change | A named owner approves in Spellbook; Data Workers queues the rerun through Airflow |
| Verification | The next run's tests pass and the SLA recovers | Checks freshness, volume and business totals on every table the fix touched |
| The record | The incident, its issues, comments and status history | A receipt: the cause, the diff, who approved it, what it touched, how it was verified and how to undo it |
Why doesn't Coalesce Quality just do this itself?
Because Coalesce drew a clear line around where its quality product acts, and it is the right line for that product. Coalesce Quality's MCP server "does not execute queries against your warehouse": it generates the SQL and you run it. Its Coalesce Transform integration "cannot modify your projects, configurations, or pipelines." Changes that would reset a check's baseline or delete a resource return requires_user_confirmation until a person agrees. Writes land in Coalesce Quality's own objects: issues, incidents, comments, data products, owners, monitors and tests.
Scout is the closest the category gets to our own wording: "Autonomous Resolution: Generates ready-to-ship code suggestions and automatically resolves pipeline issues", with test recommendations deployed "automatically as pull requests". That is a strong capability, scoped to the pipeline code Coalesce, dbt or SQLMesh runs, and Coalesce 2.0 deepens it with one context layer across Transform, Catalog and Quality. We concede that surface happily.
The Friday fix needed more than pipeline code: the Salesforce admin's change, the Stitch load, an Airflow rerun, a commission extract and a CFO dashboard, each owned by someone else. Acting across those systems takes blast-radius scoping, an owner's approval, an undo that exists before anything runs, and a record an auditor can read. For a quality tool, that means liability for systems it watches but does not own. Data Workers is built for exactly that job.
Every tool owns a slice. Data Workers covers the whole lifecycle
Coalesce Quality goes deep on quality and incidents for data products. Each point tool adds another console, contract and handoff. Data Workers covers the whole lifecycle with one context, one approval flow and one audit trail.
| Stage | Data Workers | Coalesce Quality | Why we scored it this way |
|---|---|---|---|
| Catalog & Context | 9 | 5 | Data products, domains and owners give quality a business frame, and Coalesce Catalog sits next door. Data Workers keeps one governed context graph of definitions, owners, lineage, quality and usage across every platform. |
| Analytics & Insights | 8 | 3 | Quality scores and badges tell consumers whether to trust a dashboard. Data Workers answers data questions from governed definitions with lineage behind every number. |
| Data Quality | 8 | 8.5 | Coalesce Quality's home stage: tests, contracts and SLOs at node and column level, deployment gates and AI-suggested rules. Data Workers also writes, runs and repairs checks across every platform. |
| Observability & Incidents | 8.5 | 9 | Coalesce Quality's home stage: anomaly monitors, grouped incidents, data product uptime and Scout's triage and root cause. Data Workers fixes the data across systems with approval and verifies it. |
| Pipelines & Ingestion | 8.5 | 4 | Scout drafts pipeline code fixes and test PRs. Data Workers queues reruns through your orchestrator and proposes the ingestion or source change for its owner, with approvals. |
| Schema & Migration | 8 | 5 | Schema monitors and reconciliations that prove two datasets agree. Data Workers catches schema changes at the source, scores their blast radius and plans migrations in waves. |
| Governance & Access | 8.5 | 4 | Owners, domains and alert routing are well run. Data Workers runs the warehouse access request queue: scoped, time-bound grants proposed for the owner's approval. |
| Security & Privacy | 8 | 2 | Coalesce Quality secures its own workspace and MCP tiers. Data Workers' pull request review flags new columns whose names or annotations look sensitive, and leaves a receipt on every data change. |
| Cost / FinOps | 8 | 3 | Quality checks are not a cost tool. Data Workers attributes Snowflake spend query by query and drafts the fix for its owner. |
| MLOps & Models | 7.5 | 2 | Quality monitoring can cover feature tables. Data Workers keeps the data under your models fresh and correct. |
Coalesce Quality leads on its two home stages, as it should. See how Data Workers differs from data observability, autonomous resolution and the observability tools comparison.
How Coalesce Quality and Data Workers work together
Coalesce Quality stays where SLAs breach and Scout investigates. Spellbook Data Catalog (in preview) is where the data team looks: each proposed change, who approved it and how to roll it back. Between them, Data Context Wizard keeps one governed context graph across the estate, the Data-Agents Swarm does the work with 20+ specialist agents, the Autonomous Data-Conductor runs each fix end to end, and per-domain guardrails hold approvals, receipts and rollback.
Coalesce Quality to Data Workers. Coalesce Quality connects over its API or MCP server today: your team's assistant reads issues, incidents, data products, owners and lineage side by side with Data Workers, which reads Snowflake and Airflow natively. A new incident starts a diagnosis: diagnose_incident and get_root_cause work through the evidence, trace_cross_platform_lineage follows the failure past the Coalesce node to the source, blast_radius_analysis maps everything downstream, explain_table pulls each affected table's definition and documentation, and get_incident_history checks for repeats. The repair runs through remediate: code changes are proposed as a diff for the owner to merge, source and ingestion changes go to their owners, and reruns are queued through Airflow. Verification uses run_quality_check, monitor_metrics and get_quality_score, and every step lands in get_audit_trail.
Data Workers and Scout's drafts. Data Workers reviews Scout's draft like any pipeline change: what it touches downstream and whether it matches the governed definition. Coalesce Quality keeps the incident; the receipt lives in Spellbook, and the Write tier lets the on-call's client post its link as a comment.
Side by side in one client. Coalesce Quality's remote MCP server signs in with OAuth and asks which tiers to grant: Read-only, Write, Governance or Deploy. Coalesce Transform has its own hosted MCP server, where your account's Coalesce permissions apply, and the read-only Catalog MCP lets an assistant search tables, read descriptions, traverse lineage and list glossary terms. Add Data Workers beside them: every agent is an MCP server, and the client setup guide documents the path (clone the open-source repo, add one start-agent.sh entry per agent). Example for Claude Code's .mcp.json, with Coalesce Quality's US endpoint:
{
"mcpServers": {
"coalesce-quality": { "type": "http", "url": "https://mcp.us.synq.io/mcp" },
"dw-context-catalog": { "command": "/path/to/dataworkers-claw-community/start-agent.sh", "args": ["dw-context-catalog"] },
"dw-incidents": { "command": "/path/to/dataworkers-claw-community/start-agent.sh", "args": ["dw-incidents"] },
"dw-quality": { "command": "/path/to/dataworkers-claw-community/start-agent.sh", "args": ["dw-quality"] }
}
}Ask "why is Revenue reporting stale, and what does the fix touch?" and the client calls both: Coalesce Quality returns the incident, the failed test, the SLA history and the owner; Data Workers returns the Salesforce cause, the ARR definition the fix must follow and a proposed repair with its blast radius. Coalesce Quality's consent screen scopes what the client can change there; Data Workers' guardrail decides whether a data change may run in that domain, with a named approver. A shared Data Workers endpoint takes an API key or OAuth tokens from your identity provider, verified through JWKS.
One Coalesce Quality incident, L0 to L4. The same stale data product at each level, set per domain.
- •L0 manual. An engineer applies Scout's draft and hopes ARR is right.
- •L1 observe. Data Workers posts the diagnosis: the Salesforce change, the definition conflict and everything downstream. Nothing changes.
- •L2 propose. Data Workers proposes the node diff, the guard test and the rerun. Nothing runs until the revenue analytics owner approves in Spellbook.
- •L3 act reversibly. For proven change classes, such as rerunning a halted Coalesce job after a merged fix, Data Workers queues the step, verifies it and records the receipt.
- •L4 autonomous. In a scoped domain with a long clean record, Data Workers runs the repeatable steps end to end; code changes still go to their owner.
On safety, see is it safe to let AI agents change production data, how approvals work, autonomy levels and rollback. On where data lives: the agents run in your infrastructure, your data stays in your systems, and the hosted Conductor sees workflow metadata only.
What changes for your team
Coalesce Quality gave every data product an owner and an SLA. Data Workers gives those owners a crew.
- •Incidents. A Coalesce Quality incident gets a cross-system diagnosis, a blast radius and a proposed fix, and closes with a receipt.
- •Data quality. Each fix leaves a guard test on the node that broke, inside the project Coalesce Quality already watches.
- •Cloud spend. Snowflake spend is attributed query by query, and each fix goes to its owner drafted.
- •Access. A warehouse access request becomes a scoped grant with an expiry date, proposed for the data owner to approve.
- •Audits. Coalesce Quality records the incident; Data Workers records what changed in the data, who approved it and how to undo it.
- •Migrations. A move off a legacy warehouse runs in approved waves, with parity checks planned and tracked for each wave.
The data product owner changes most: one proposal to review at 7:50 a.m. instead of a morning between Salesforce, Snowflake and a Slack thread. See the data incident response playbook and who owns the agents.
Keep Coalesce Quality, or consolidate?
Keep Coalesce Quality if you love it; Data Workers works with it from day one. Many teams consolidate once Data Workers runs that slice too.
Most Coalesce teams keep it: the tests sit next to the nodes, data products and owners are defined, and Coalesce 2.0 ties quality to the same context layer as Transform and Catalog. What teams consolidate is the stack around it: a second monitor on the same break, hand-written fix scripts and the Slack thread that is the only record of last quarter's fix. Building the fix layer yourself on Coalesce's MCP servers? Read build it ourselves with Claude Code and MCP servers: the MCP calls are easy; the context graph, approvals and rollback are the work. For the category and this stack, see you're on Elementary, you're on Monte Carlo, you're on Soda, you're on Coalesce, you're on Coalesce Catalog, you're on Airflow, you're on Stitch, Data Workers on Snowflake, Data Workers integrations and what is an agentic data platform.
The case for your CFO
The outcome: ARR, pipeline and commissions come out of the "Revenue reporting" data product, and Coalesce Quality already tells your team the moment it goes stale. Data Workers turns each incident from a morning of tracing into a diagnosis on arrival and a complete fix with one approval, checked against the definitions finance signed off, so the numbers are right before anyone reads them.
The risk story is plain. Autonomy is set per domain: at L1 Data Workers only reads; at L2 it changes nothing until a named person approves; at L3 it applies changes it can undo. Code changes go to their owner as a diff. An unanswered approval request expires and escalates, never auto-grants. No agent can promote its own work. Every change carries a receipt: the cause, the diff, who approved it, what it touched, how it was verified and how to undo it. An org-wide stop halts all autonomous dispatch. Zero migration: Coalesce, Salesforce, Stitch, Snowflake, Airflow and Omni stay where they are.
Why now: Scout already drafts the code fix; the slow part is matching the business and carrying the fix through systems Coalesce does not run. The first win is read-only: every incident on one data product gets a cross-system diagnosis and a blast radius. What stays the same: your data products, SLAs, tests, Git review, Snowflake permissions and Airflow schedules. For the numbers, see the ROI of agentic data operations.
The sentence to repeat upstairs: "Coalesce Quality tells us which data product broke; Data Workers fixes it across the stack, checked against our definitions, with an approval, and proves it held."
Getting started
Start with a pilot. Pick one data product, such as Revenue reporting, connect Data Workers to Coalesce Quality, Snowflake and Airflow, and run at L1 so every incident gets a diagnosis and a blast radius. Then turn on the first write class at L2, such as reruns of a halted Coalesce job with owner approval. The pilot path and plans are on the pricing page, and the pilot is credited in full against the first year.
FAQ
Is SYNQ the same product as Coalesce Quality? Yes. Coalesce announced its acquisition of SYNQ and the launch of Coalesce Quality in March 2026. The documentation still lives at docs.synq.io, the MCP endpoints use synq.io hosts, and Scout keeps its name.
How does Data Workers connect to Coalesce Quality? Over Coalesce Quality's API or MCP server today, reading issues, incidents, data products, owners and lineage; Snowflake and Airflow are native. You can also run both MCP servers in one client and ask both in one conversation.
Scout promises autonomous resolution. Why add Data Workers? Scout resolves pipeline issues in the code Coalesce, dbt or SQLMesh runs, and does it well. Many incidents start outside that code: a source change, an ingestion setting, a definition the business owns. Data Workers traces to that cause, checks the fix against governed definitions, routes each change to its owner under one approval and verifies the result.
Which MCP permission tier does our assistant need in Coalesce Quality? Read-only covers the diagnosis. Grant Write to post the receipt link as an incident comment. Governance and Deploy stay with the people who manage data products, owners, monitors and tests.
Does Data Workers change our Coalesce Transform nodes? It proposes node changes as a diff for the owner to merge through your normal review, then queues the rerun through your orchestrator and verifies the result.
Does Data Workers change anything in Salesforce or Stitch? No. Source and ingestion changes go to their owners as a proposal with the evidence attached. Data Workers reads the effect in Snowflake, proposes the pipeline fix and verifies the result.
Sources
- •Coalesce, "Coalesce Announces Acquisition of SYNQ and Launch of Coalesce Quality" (newsroom, Mar 9, 2026; release dated March 10, 2026), https://coalesce.io/company-news/coalesce-announces-acquisition-of-synq-and-launch-of-coalesce-quality/ (checked Oct 3, 2026)
- •SYNQ homepage ("SYNQ is now Coalesce Quality"; Scout, the data quality AI agent), https://www.synq.io/ (checked Oct 3, 2026)
- •Coalesce, Coalesce Quality product page, https://coalesce.io/product/quality/ (checked Oct 3, 2026)
- •Coalesce homepage (Transform, Catalog, Quality; Scout), https://coalesce.io/ (checked Oct 3, 2026)
- •Coalesce, "Coalesce 2.0: The Agentic Data Engineering Platform" (Sep 15, 2026), https://coalesce.io/product-technology/coalesce-2-0-the-agentic-data-engineering-platform/ (checked Oct 3, 2026)
- •Coalesce, Coalesce Catalog product page, https://coalesce.io/product/catalog/ (checked Oct 3, 2026)
- •Coalesce Quality Docs, Scout, https://docs.synq.io/scout/scout.md (checked Oct 3, 2026)
- •Coalesce Quality Docs, MCP server (endpoints, OAuth, permission tiers), https://docs.synq.io/scout/mcp.md (checked Oct 3, 2026)
- •Coalesce Quality Docs, synq-scout agent workflow, https://docs.synq.io/scout/agent-workflow.md (checked Oct 3, 2026)
- •Coalesce Quality Docs, Data Products overview, https://docs.synq.io/data-products/data-products-overview.md (checked Oct 3, 2026)
- •Coalesce Quality Docs, Incidents overview, https://docs.synq.io/incidents/incident-overview.md (checked Oct 3, 2026)
- •Coalesce Quality Docs, Coalesce Transform integration, https://docs.synq.io/coalesce-integrations/coalesce-transform.md (checked Oct 3, 2026)
- •Coalesce Quality Docs, Reconciliation overview, https://docs.synq.io/reconciliation/overview.md (checked Oct 3, 2026)
- •Coalesce Docs, Configure Transform MCP, https://docs.coalesce.io/docs/coalesce-ai/mcp/configure-mcp.md (checked Oct 3, 2026)
- •Coalesce Docs, Catalog MCP, https://docs.coalesce.io/docs/reference/glossary/catalog-mcp.md (checked Oct 3, 2026)
- •Coalesce Docs, Schedule Coalesce Jobs with Apache Airflow, https://docs.coalesce.io/docs/deploy-and-refresh/third-party-devops-tools/orchestration/schedule-coalesce-jobs-with-apache-airflow.md (checked Oct 3, 2026)
- •Data Workers open-source repository, https://github.com/DataWorkersProject/dataworkers-claw-community (checked Oct 3, 2026)
- •Data Workers client setup guide, https://dataworkers.io/opensource-docs/client-setup/ (checked Oct 3, 2026)