mcp
Is access enough? Auth patterns for Claude Managed Agents
MCP tunnels let Claude Managed Agents reach and authenticate to real infrastructure, but access alone doesn't make an agent one you'd trust to run on-call.
Ben Yemini
causal reasoning
Introducing Issues, one problem, one thread, start to finish.
On-call means re-triaging the same problem as diagnoses shift underneath you. Causely's new Issues give people and agents one stable thread instead.
Expanded Dynatrace Support: Better, Faster, Cheaper Agents
Causely turns Dynatrace entities, topology, and alerts into a causal model that names the root cause, making on-call agents better, faster, and cheaper.
AI
Your on-call AI agent is wrong more than you think
Most frontier LLMs degrade badly by ~1,000 tokens of input, not the millions in their spec sheets. For on-call agents, that means accuracy drops exactly as an incident gets complex. The fix isn't a bigger model. It's not handing the LLM the raw data at all.
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%.
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.