Data Observability, Reimagined For the Age of AI.

A new era in data quality starts now. Built on our powerful AI-native platform, Sifflet’s agents take observability beyond alerts and into action. Detect subtle signals. Triage in seconds. Resolve with confidence. This is what AI-first data reliability looks like.

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Three Agents,

One Mission.

Introducing

Sentinel

agent

Sentinel stands guard over your entire data estate, not by scanning rows, but by reading the signals in your metadata.

It monitors lineage, freshness patterns, schema drift, and usage across your stack to uncover what matters most.

Then it recommends strategic, risk-aware monitors before anything breaks. Vigilant. Contextual. Always on duty. You choose to activate based on precision, context and cost.

Sentinel agent

In action

Sentinel designs your monitoring strategy before incidents happen.

At a growing e-commerce company, daily_orders is a mission-critical table powering dashboards for Finance, Growth, and Ops.

As soon as Sentinel connects to the data stack, it starts analyzing metadata.

It learns:

Based on this context, Sentinel recommends:

A freshness monitor tied to update timing

A null-check on order_total

A distribution alert on item_count

These aren’t generic rules, they’re risk-aware suggestions grounded in business impact, historical incidents, and usage patterns.

Your team approves with one click. Sentinel handles the metadata deep dive and you stay in control.

Introducing

Sage

agent

Sage brings wisdom to your observability stack. When issues arise, it sees the full picture, both past and present. Drawing on lineage, code changes, query logs, and historical incidents, Sage delivers a real-time narrative of what went wrong, why, and who’s impacted. Less time investigating. More time knowing.

Sage agent

In action

Sage investigates the issue and connects the dots.

A pull request merged yesterday that removed a fallback for item_count in calculate_metrics.sql

A failed transformation job in staging_orders that blocked item_count from populating

Broken correlation between item_count and order_total

Similar incidents from March and November where missing values inflated AOV

Lineage : orders_raw → staging_orders → daily_orders → aov_metrics

Downstream dashboards used daily by Finance and Growth

Sage’s output:

Sage gives you the clarity of a senior engineer’s intuition, instantly, repeatably, and with full traceability.

Introducing

Forge

agent

Forge is your builder-in-the-loop. It studies past fixes, identifies patterns, and drafts tailored solutions grounded in your own operational history. When Sage uncovers the root cause, Forge gets you ready to resolve it fast. With clear code suggestions, context, and traceability, it delivers the blueprint. You stay in control.

Forge agent

In action

Forge drafts the fix based on what’s worked before.

With Sage’s incident story in hand, Forge steps in to guide the resolution process.

It scans historical incidents, compares code patterns, and pulls from institutional memory, giving your team a clear path forward.

Here’s what Forge finds:

Forge suggests:

Forge doesn’t just advise. It delivers a blueprint your team can trust, faster than ever.

AI isn’t an Add-On.

It’s the Engine.

Sifflet was built with AI at the very core. It doesn’t rely on static thresholds or reactive alerts, it learns how your data behaves, anticipates what’s abnormal, and surfaces issues before they become problems. No configuration. No guesswork. Just intelligent observability that gets sharper with every signal.

Simoh-Mohammed Labdoui

Head of Data, Saint-Gobain

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape, without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Sanjeev Mohan

Principal, SanjMo

The shift towards distributed, agent-based architectures represents a significant evolution in data observability. By embedding intelligence and context-aware resolution directly within the metadata layer, this approach moves beyond traditional static monitoring, enabling teams to transition from reactive alerting to proactive and autonomous remediation. This trend, exemplified by Sifflet, signals a key direction for the future of the data observability landscape.

George Firican

LightsOnData

The future of data observability lies in empowering data teams with intelligent support. Sifflet’s agentic approach, through Sentinel, Sage, and Forge, introduces systems that act less like tools and more like collaborators. By combining metadata awareness with historical system memory and contextual guidance, they help teams focus, prioritize, and deliver greater impact with confidence. This signals a new era where observability becomes a proactive force behind data-driven innovation that scales not just with data, but with people.

All features you can take advantage of, starting today.

Adaptive anomaly detection

No manual thresholds required.

Intelligent pattern recognition

Across time, seasonality, and schema

Context-rich diagnostics

Using lineage, code changes, and metric drift

Noise suppression

Through automated signal correlation and grouping

Limited Access Available in Private Beta

See what agentic data observability can do for your organization. Join our private beta while spots are still available.

Get Access in Private Beta