Synq's Coalesce Quality platform tracks data quality monitors, incidents, lineage, and alerts across connected data systems, and an agent working inside Neotask can pull all of that directly into conversation through this connection. Ask it to review open incidents and it pulls the current list along with severity and affected tables. Ask it to trace where a broken metric originates and it can walk the lineage graph back to the source table or job that introduced the issue, rather than leaving someone to click through a dependency graph manually across several linked screens. It can also check whether a monitor has fired recently and summarize the alert history for a given dataset, which is normally something a data engineer would check by logging into a separate dashboard and filtering by date range. The result is that observability work, investigating an incident, tracing lineage, reviewing alert history, happens in the same place you're already asking the agent to do other work, without a separate login or a context switch away from whatever else you're focused on that day. That matters most in the middle of an incident, when the time spent switching between tools is time a broken dashboard stays broken.
| List incidents | Pull currently open data quality incidents and their severity across your connected systems |
| Review monitors | Check the status of data quality monitors across connected systems |
| Trace lineage | Follow a table or metric's lineage back to its source, step by step |
| Review alerts | Retrieve recent alert history for a specific dataset or monitor |
| Investigate issue | Pull incident details, affected downstream tables, and related context in one summary |
| Identify affected downstream tables | Determine which reports or dashboards depend on a table with an open incident |
A data engineer notices a revenue number looks wrong and asks the agent to check for open incidents on that table. The agent finds an active incident, traces the lineage back to an upstream job that failed overnight, and reports which downstream dashboards are affected so the team knows the scope before fixing the root cause. It also notes when the monitor first fired, which helps narrow down exactly when the bad data started flowing into the warehouse. Because the agent already has the lineage mapped out, the engineer can go straight to the failed job instead of guessing at which of several upstream tables is actually responsible. Once the job is rerun, the engineer asks the agent to confirm the incident has actually cleared before telling stakeholders the dashboard is fixed.
Ahead of a team standup, someone asks the agent to summarize the past week's monitor alerts across the warehouse. The agent pulls the alert history, groups it by dataset, and highlights which monitors fired more than once, giving the team a starting point for what to prioritize that week. It also flags any monitor that has been repeatedly noisy, which is often a sign the threshold itself needs adjusting rather than that something is actually broken. The team walks into standup with a ranked list instead of everyone individually checking their own piece of the pipeline. Someone follows up mid-meeting asking about a dataset that wasn't on the summary, and the agent checks it live, confirming it has stayed clean all week.
It can surface incidents, lineage, and alert history to help you diagnose the problem, but resolving the underlying pipeline issue is still done by your team once the root cause is clear.
It can pull both currently open incidents and historical alert data for a given monitor or dataset, so you can look at a single day or a longer trend.
It follows the lineage graph as recorded in Coalesce Quality, tracing a metric or table back through its upstream sources as far as that lineage is mapped in your connected system.
No, the agent reads directly from your connected Coalesce Quality account, so most investigation can happen without opening the dashboard separately, though the dashboard is still there for deeper visual exploration.
Yes, anyone with access to the Neotask agent and the connected account can ask about the same incident or dataset independently, which is useful during a shared investigation.
Yes, by reviewing alert history over a period you specify, it can point out which monitors fire most frequently, which is often the first sign a threshold needs retuning.