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:

daily_orders updates every morning via ETL

It sits downstream of volatile vendor feeds

It’s queried hundreds of times per day

It powers KPIs used in board meetings and marketing spend decisions

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.

A freshness monitor tied to update timing

A null-check on order_total

A distribution alert on item_count

The data team is prompted with this recommendation and can choose to activate the monitor with a single click, ensuring they remain fully in control of what gets deployed.

With the team’s approval, the monitor goes live to track that pipeline’s health, and Sentinel even advises on the fix by proposing a reprocessing job (ETA: 12 minutes) to restore the missing data.

In this way, Sentinel acts as a smart assistant that learns normal behavior, catches anomalies early, and helps the team respond rapidly — while leaving final decisions, like deploying monitors, firmly in the hands of the user.

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

At 7:10 a.m., Sentinel triggers a high-priority alert:

Related Signals:

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.

Minutes after a monitors flags an abnormally high AOV in the daily_orders table, Sage takes over.

It starts building a real-time incident story from metadata, surfacing signals your team would spend hours piecing together:

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:

Probable Root Cause:
‍item_count nulls inflating AOV due to missing fallback logic

Change History:
PR #2410, merged April 30

Suggested Reviewer:
@jen-dataeng

Next Step:
Reinstate default value or enforce item_count integrity upstream

Sage gives you the clarity of a senior engineer’s intuition, instantly, repeatably, and with full traceability.
It also surfaces business context
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:

Incident #1192 from March 2024, where the same issue was resolved by adding a coalesce fallback for item_count

A similar PR written by @jen-dataeng, complete with reviewer history and rationale

A removed safeguard in PR #2410 that triggered the current issue

Slack thread linking that change to a past debate on fallback defaults

Forge suggests: