AI-Powered Observability for Your MongoDB Atlas Application Flows

Most teams using MongoDB Atlas struggle with the same observability trap: too much noisy, low-level telemetry and not enough context to actually solve problems.

Manual instrumentation is tedious, and auto-instrumentation floods you with data that still leaves blind spots.

This post reveals how an AI-powered observability autopilot fixes that problem—giving you leaner, business-aware telemetry that reduces costs and speeds up incident resolution.

AI-Powered Observability for Your MongoDB Atlas Application Flows

"You can't improve what you can't measure" goes the well-known adage.

Companies are adopting ever more sophisticated observability platforms to make sense of their increasingly complex IT systems.

However, in the face of skyrocketing costs for telemetry data, the promise of optimized reliability and shorter time-to-resolution can only be realized if the quality, not the raw quantity, of collected signals is improved.

Auto-instrumentation of popular IT platforms generates huge amounts of low-level data, but what is lacking is the business context - and consistent labeling - for humans or AI-assisted analysis to make sense of what is happening.

Unfortunately, manual instrumentation of a code base is a tedious, time-consuming process that often falls behind other engineering priorities. It's only when disaster strikes at 2am and systems are down that engineers wish they had better data.

You want high-quality signals - logs, metrics, traces with the appropriate business context - for your end-to-end application flow, including your Atlas database, without incurring the high cost of building and managing all the telemetry.

Introducing Sylogic, the Observability Autopilot

Sylogic is a new AI-powered observability expert that can do the following:

• Create a catalog of existing instrumentation across your code base, with details about the semantics, signals, and target observability platform
• Detect business flows, applications and APIs from your code base, and recommend appropriate Service Level Indicators (SLIs) and Service Level Objectives (SLOs) based on our curated best practices and your policies
• Generate a gap analysis of your observability posture, and suggest remediations
• Generate git pull requests to configure auto-instrumentation and add custom signals with high accuracy, consistent labeling and business context for precise correlation and business impact analysis
• Migrate your instrumentation to OpenTelemetry, including installation and configuration
• Manage your overall observability posture according to policies expressed in natural language, establishing and maintaining standards across your organization
• Allow you to interrogate your system architecture and observability data via chat, producing diagrams, summaries, tables and dashboards to facilitate system insight, root cause analysis, and optimization opportunities

Sylogic: The Observability Autopilot

Essentially, you now have a new expert, undefatigable (virtual) member of your team who relentlessly manages your observability posture, both for application logic as well as for data flow.

Engineers and Product Managers address their observability needs via the catalog, while Sylogic does the rest, keeping the code base up to compliance to the policies expressed in the catalog.

The result is a dramatic increase in coverage and quality of the telemetry you collect, while reducing cost for less useful, low-level data, leading to much improved KPIs across the spectrum: Fewer incidents, reduced MTTD and MTTR, better data quality, happier customers.

Using Sylogic with OpenTelemetry to Build and Manage Observability for MongoDB Atlas Applications: Use Cases

OpenTelemetry provides a powerful way to shine a light on MongoDB's query performance. By using OTel's MongoDB instrumentation libraries (available for many languages/drivers), Sylogic can auto-capture trace spans for each database operation.

Every time an application code calls MongoDB (e.g. a .find() or aggregation), OTel can record: the command, timing, and relevant tags (collection name, number of documents returned, etc.). These spans are exported to a monitoring backend (e.g. Jaeger, Tempo), where they become searchable and visualizable. This yields several benefits:

Automated Instrumentation

Sylogic can provide an OTel auto-instrumentation agent for MongoDB drivers (leveraging community packages like the Node.js and Python OTel MongoDB instrumentation).

We helped a startup automatically instrument their entire code flow, from data ingestion to business intelligence, enabling alerts for data quality issues and data processing errors or slow-downs.

Detection of Slow Queries

With OTel traces, the system can automatically flag queries exceeding a latency threshold.

For instance, if a query normally takes 50ms but now regularly takes 500ms, an alert can be triggered. A "Slow Queries" view can rank operations by duration to help developers zero in on the worst performers.

Performance Anomaly Alerts

By collecting metrics (via OTel Metrics API) on query throughput, execution times, and perhaps even nReturned vs nScanned (from explain plans), one can apply anomaly detection.

For example, a sudden spike in documents scanned per query could indicate a query plan regression. OTel's data can feed an AI model or heuristic to detect such anomalies in real time and alert the ops team.

Contextual Tracing

Crucially, OpenTelemetry ties MongoDB spans to the end-to-end trace of a request.

This means when a microservice call or a user transaction triggers multiple actions (some cache calls, some MongoDB queries, some external API calls), all those spans share a common trace ID.

In practice, this allows a distributed trace view: you can see that Service X received a request, then made a MongoDB query that took 800ms (the bottleneck), then continued.

This context is invaluable.

Instead of just knowing "MongoDB query slow," engineers see which higher-level operation suffered. For example, an e-commerce trace might show the checkout API call took too long because a MongoDB payment history query was slow – pinpointing blame.

Without OTel, developers often struggle to connect these dots across system boundaries.

Performance Dashboards & Analytics

On the Sylogic platform, the incoming OTel data can be aggregated into intuitive dashboards.

For example, a "MongoDB Query Performance" dashboard could display: top N slow queries, their frequency, and which services or endpoints call them most. It can also chart latency over time and correlate with releases.

Alerts and Anomaly Detection

Sylogic.ai can implement alerting rules on the telemetry data.

For instance: "Alert if any query's 95th percentile latency exceeds X ms" or "Alert if throughput drops by Y% (possible stuck operations)".

Using OTel metrics, thresholds for MongoDB resource usage (connections, operation counts, etc.) can also be set.

In one example, a company combined OTel with Prometheus to catch a spike in Kafka-to-Mongo pipeline latency, improving fraud detection times by 40%.

Get Started Today

Reach out to us at Sylogic.ai to learn more, get an observability gap report and start a pilot!

Transform your MongoDB Atlas observability from reactive firefighting to proactive, intelligent monitoring with AI-powered automation that actually understands your business context.