What is Causal AI and Why Do DevOps Teams Need it?
Causal Reasoning Platform
Causal AI for DevOps
Causely solves the root cause analysis problem
Monitoring and observability tools create a flood of detected anomalies when a root cause occurs anywhere in the environment. DevOps and SRE teams waste endless cycles in triage and troubleshooting, while application performance degrades and business is disrupted.
In this environment, applications’ reliability is NOT continuously assured.
Causely solves the root cause analysis (RCA) problem, automatically pinpointing root cause based on observed symptoms.
Assure continuous application reliability with Causely
How Causely works
- Our Causal Reasoning Platform is shipped with out-of-the-box Causal Models that drive the platform’s behavior.
- Once deployed, Causely automatically discovers the application environment and generates a Topology Graph of it.
- A Causality Graph is generated by instantiating the Causal Models with the Topology Graph to reflect cause and effect relationships between the root causes and their symptoms, specific to that environment at that point in time.
- A Codebook is generated from the Causality Graph.
- Using the Codebook, our Causal Reasoning Platform automatically and continuously pinpoints the root cause of issues.
Causal Models are the knowledge base
- Provided out of the box, applicable to any environment
- Capture potential root causes in a broad range of entities
- Describe all the potential root causes and the observed symptoms they may cause
Topology Graph is the cartographer
Causely automatically discovers and maps dependencies among the tangled web of applications, services, and complex, dynamic infrastructure, such as:
- All entities and how they relate to each other
- Connectivity relationships
- Layering relationships
- Composition relationships
- Real-time updates to continuously reflect current state
Causality Graph maps cause & effect relationships that are beyond human scale
A Directed Acyclic Graph (DAG) is generated by instantiating the Causal Models with the Topology Graph.
- The Causality Graph represents all possible root causes, symptoms that may be observed, and cause and effect relationships between them
- Nodes in the Causality Graph represent root causes and symptoms
- Edges represent causality and are labeled with its probability
- The Causality Graph automatically updates as the topology of the environment changes
Codebook translates symptoms to root cause
A Codebook is automatically generated from the Causality Graph.
- The Codebook is a causality table where the columns R1,…,Rn represent all the potential root causes, the rows S1,…,Sm represent all the potential symptoms and a cell (Ri,Sj) represents the probability root cause Ri may cause symptom Sj, i.e., the likelihood symptom Sj will be observed/present when root cause Ri occurs
- Each root cause represented in the Codebook has a unique signature, a vector of m probabilities, that uniquely describes each possible the root cause
- A continuous real-time search of the Codebook is performed to pinpoint root causes based on the observed symptoms in the environment
- The Codebook continuously updates as the topology of the environment changes
See it for yourself
Causely for Cloud-Native Applications, built on our Causal Reasoning Platform, detects and remediates application failures in complex cloud-native environments.
Tour the productStreamline remediation
The Causely UI represents root causes, their related symptoms, service impacts, and enables remedial actions to be taken in context of root causes.
From root causes, Causely can also initiate incident remediation workflows in other systems. This might include alerting teams responsible for root cause resolution as well as notifying teams whose services are impacted by root cause so they are aware of these situations and who is responsible for the resolutions. It might also include initiating remediation workflows in orchestration tools (CI/CD, IAC, Automation Scripts, etc.)
Learn moreProactive reliability engineering
As well as automating the incident response process, Causely also provides valuable insights to support proactive reliability engineering activities. These include:
- “What if” questions to support proactive planning of changes and maintenance activities
- Highlighting intermittent recurring incidents so action can be taken to avoid recurrences
- Providing detailed explanations for the root cause and effect of prior incidents to streamline postmortems
- Understanding what the effect of service degradations, congestions and malfunctions will be before they happen so the resilience of services and infrastructure can be improved
Built for modern, dynamic environments
Causely is built to handle today’s constantly evolving environments where new services are being deployed and existing ones are ever-changing.
As entities and relationships within your environment change, our Causal Reasoning Platform automatically updates its Topology Graph, Causality Graph and Codebook. This ensures that Causely’s knowledge and troubleshooting capabilities are constantly aligned with your current configuration.
View integrationsLearn more
Watch the webinar
Causely for Cloud-Native Applications
Beyond the Blast Radius: Demystifying and Mitigating Cascading Microservice Issues