AI
Why AI agents burn tokens on every reliability query
AI agents reconstruct environment state from raw telemetry on every reliability query. Causal context eliminates the reconstruction and cuts token use by 60%.
Ben Yemini
Blog
How Humm Group Delivered Flawlessly in a High-Stakes Product Launch
Launching a new fintech product required certainty across a complex microservices platform. With Causely modeling cause-and-effect relationships across services, Humm Group gained system-level understanding and confidence that critical dependencies behaved correctly during launch.
Causely product
Alerts Aren’t the Investigation: What Comes Next in Incident Response?
Alerts are signals, not explanations. By explicitly mapping alerts to symptoms and inferred root causes, Causely turns alert noise into a coherent explanation of what is actually happening in the system.
Causely product
When Asynchronous Systems Fail Quietly, Reliability Teams Pay the Price
Causely’s causal model has been expanded for asynchronous messaging systems. Instead of treating queues as opaque buffers, Causely models messaging infrastructure as it operates in production, making asynchronous failures explicit and explainable.
integration
Causely Expands Datadog Integration to Deliver Causal Intelligence Across Hybrid Environments
Causely’s expanded Datadog integration turns Datadog APM signals into system-level causal intelligence, helping teams understand how issues propagate across services and pinpoint true root cause.
Blog
Announcing Reliability Delta: Clear, Objective Insight into Whether Your Release Made Your System Better or Worse
In a 50 to 100+ microservice environment with dense service-to-service dependencies, even small regressions can cascade silently. And slowing down isn’t an option. Leadership needs faster delivery and fewer incidents. This is why we built Reliability Delta.
Blog
Introducing Causely’s MCP Server for Automated Remediation in Kubernetes and Beyond
The Causely MCP Server brings our Causal Reasoning Engine directly into the IDE so engineers can understand why incidents happen and apply the right fix at the right layer, whether that’s runtime, configuration, or code.
Blog
Zero Downtime at 30,000 RPS: How Quantum Metric Rearchitected with Causely
During a high-risk migration, Causely gave Quantum Metric a new kind of clarity rooted in cause-and-effect across dynamic systems. This helped them improve how they think about managing complexity at scale and move fast without breaking things.
Blog
How a Leading Gaming Platform Prevented Revenue Loss Using Causely in Grafana
With Causely + Grafana, the gaming platform can spot reliability risks early, take the right action, and avoid revenue-impacting incidents before users even notice.
Causely product
Causely Now Integrates With incident.io to Accelerate Incident Response
By combining Causely’s causal reasoning engine with incident.io, engineering teams with complex microservices environments can go from incident to resolution much faster.
Blog
OpenTelemetry Logging Best Practices to Avoid Drowning in Data
We’ll recap OTel logging best practices, explore how to use logs effectively in troubleshooting without drowning in data, walk through a tutorial workflow you can apply today, and show how Causely operationalizes this approach automatically at scale.
Causality
Why Standalone Docker Matters: 4 Patterns Every Scalable Architecture Should Consider
This post explores four architecture patterns where standalone Docker is not only justified but recommended.