Rethinking Data Observability in the Age of AI: Introducing Scout

Recently, we hosted a live webinar to talk about the evolution of data observability: what’s changing, what’s broken, and how SYNQ is building for what’s next. Our CEO & Co-Founder Petr Janda joined by our Field-CTO Stepen Murphy walked through the macro trends reshaping data workflows, where we see AI adding the most value, and how Scout, our autonomous AI agent, is helping customers resolve data quality issues before they become incidents.

If you missed it, here’s the recap.

From Metadata to Intelligence: The Evolution of Data Observability

To frame the discussion, we need to start by zooming out. Every data team operates along the same development lifecycle: plan, build, test, operate, analyze. Historically, observability has lived squarely in the “operate” phase. It’s been reactive. You collect metadata. You monitor volume, freshness, maybe run some tests. You get an alert, you triage manually.

But with the rise of AI, that’s no longer enough.

We’re seeing AI reshape every phase of the data lifecycle and not just analytics and code generation, but the observability layer in between. And that’s where Scout comes in.

Not Just Smarter: Actually Autonomous

Scout is not a chatbot. It’s not an assistant you have to prompt. Scout is an agent that’s always-on, operating autonomously, analyzing metadata, running diagnostics, and surfacing recommendations proactively.

The difference matters.

• Chatbots are reactive. They wait for a prompt and then respond with information. They're helpful for documentation lookup or support-style tasks, but they don't initiate workflows or handle complex multi-step processes.
• Assistants go a step further. They can take actions, guide you through workflows, and offer suggestions. But they still require frequent human input and direction. They rely on being "in the loop" and aren't designed to operate independently.
• Agents, like Scout, are designed to act without being asked. They watch your data systems continuously, detect issues, analyze context, and propose actions automatically. Agents make decisions, triage noise, and only escalate when necessary. They're built to reduce operational burden, not add to it.

Scout is that agent. It doesn’t need to be pinged. It works behind the scenes: replicating queries, tracing lineage, checking logs, and scanning code. It shows up only when it has something important to say. It's observability without the overhead.

Enter Scout: Your Always-On AI Data SRE

So what can Scout actually do for your team?

Scout is an autonomous data quality agent that runs in the background. It recommends the right tests to deploy, triages failing ones, and in many cases, resolves the issue before a human ever needs to step in.

What used to take 30–40 minutes of manual triage now happens in seconds.

Here’s what it does today:

• Testing Advisor: For any table, Scout understands the schema, lineage, usage patterns, and downstream impact. It recommends the most relevant tests based on real context.
  
• Automated Triage: Scout reviews failed tests, traces upstream dependencies, analyzes logs and commits, and surfaces root cause, all within seconds.
  
• Human-in-the-loop Control: While Scout automates the heavy lifting, it doesn’t make changes unilaterally. Practitioners always stay in control, with full visibility and override options.
  

Built for How You Work

Scout works where you do. It plugs into your transformation layer, whether that’s dbt or SQLMesh, and opens pull requests directly in your repo. 

It also integrates with the broader data stack: warehouses like Snowflake, BigQuery, and ClickHouse; orchestration tools such as Airflow; and BI platforms like Looker and Omni. For version control, Scout works with GitHub and GitLab, using commit history and code context to enhance diagnostics. This ecosystem-wide integration gives Scout the full picture: lineage, ownership, usage, so it can surface more accurate recommendations and resolve issues faster.

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And we designed deployment to be flexible. Whether you want to run it fully in the cloud, hybrid, or fully on-prem: we support all three.

In Production, Today

This isn’t vaporware. Scout is live with customers today. Check out the video to see how Scout operates in an actual environment.

Watch the Full Session

If you want to see Scout in action or hear more about how AI is transforming the data lifecycle, check out the full recording below.

And if you’re ready to start improving your data quality with SYNQ + Scout, book a demo.

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