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Architecture

Overview

Cognigy.AI is a modern, cloud-native software solution based on a scalable microservice architecture. This modern architecture allows us to leverage the compute power of multiple servers for a single software product. Instead of running a single executable on a single server, we have de-composed Cognigy.AI into more than 30 separate executables. These smaller executables (hence the name microservice) despite being separated, act as a single product - Cognigy.AI - by interacting with each other through network calls.

Cognigy.AI v4.21.0 architecture

Each individual microservice runs as a containerized application on top of Kubernetes. Containers are a way of how processes can run in isolation from each other and offer the ability to ship modern software products together with their dependencies - for instance a runtime environment. A program written in JavaScript might need a specific version of the NodeJS runtime to function properly - containers allow to package such dependencies together with the actual application into one shippable unit. One of the more popular container runtimes and a set of other container specific tools is Docker.

Managing a high numer of moving parts - the microservices mentioned above - is quite a challenge and requires an additional software product which is called a container orchestrator. Kubernetes is such a container orchestrator dealing with tasks like:

  • assigning containers to one of the available servers in the Kubernets cluster respecting their individual hardware requirements and matching these against the available hardware on a server
  • observing whether containers need to be restarted in case they crash
  • updating containers to a newer version when a software update (e.g. new version of Cognigy.AI) has been shipped
  • testing whether containerized processes still respond by implementing readiness- and liveness-probes

Runtime & IDE

Cognigy.AI itself is a product offering different groups of functionality:

  • IDE
  • Runtime
Components for Cognigy.AI IDE (orange), Cognigy.AI Runtime (green) & Cognigy Insights (blue)

Cognigy Insights plays a smaller role regarding Cognigy.AIs product architecture, hence we skip it on this page. You can learn more about Insights (Analytics) in our product documentation dedicated for it.

IDE - building your Virtual Agents

A place that allows you to create Virtual Agents and use all of the functionality like Flows, Playbooks, Lexicons & Intents. We call this part of the product the IDE (=Integrated Development Environment) - a term often used in software development. Once you are done with the first iteration of your Virtual Agent, a so-called Snapshot must be created in order to deploy your Virtual Agent into production. A Snapshot is an immutable copy of your Virtual Agent containing all resources which make it up - e.g. your Flows, Lexicons, Intents and Extensions. Snapshots act as the interface to the second group of functionality: The Cognigy.AI Runtime.

The following table shows the most relevant microservices forming the IDE:

Microservice name Responsibilities
service-ui Serves the WebGUI for Cognigy.AI
service-api RESTful API to fully control Cognigy.AI through HTTP calls. Full documentation in the form of an OpenAPI spec is available. Also implements authentication & authorization.
service-resources Manages all resources within Cognigy.AI - meaning Flows, Endpoints, Playbooks etc.
service-custom-modules Processes uploaded Cognigy Extensions and prepares them for usage.
service-security Manages users, organizations and roles including permissions.
service-handover Implements an interface for 3rd party handover providers like Ring Central Engage.

Runtime - running your Virtual Agents

Our Runtime is optimized for performance & throughput. All microservices belonging to the runtime are stress-tested on a regular basis and can scale horizontally. Cognigy.AI's runtime is a highly distributed system and can leverage huge amounts of hardware if given. The following table describes the responsibilities of key runtime microservices:

Microservice name Responsibilities
service-endpoint Translates channel specific incoming requests to our internal representation, executes Transformers, counts billing relevant information and sends responses back to external channels.
service-ai Processes actual user messages, coordinates with NLU stack, waits for Extensions and HTTP Requests to external systems and processes Cognigy Script.
service-nlp-ner Detects System Slots in user utterances - these are things like dates, emails, currencies and numbers.
service-nlp-matcher Detects Lexicon Slots in user utterances using Lexicons.
service-nlp-score Implement intent recognition based on Intent models our customers can train within our platform. There are variants for different languages (German, English, Korean, Japanese, Generic, XX).
service-http & execution Sends HTTP-Requests to external 3rd party systems when HTTP Request Flow Node is used and executes Flow Nodes of our customers as part of Extensions.
service-profiles Manages Contact Profiles and offers the ability to access them via Cognigy Script.
service-function-scheduler & function-execution Schedules and executes the source-code of our customers in Cognigy Functions.
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