Glossary

Centralized registry and management system for discovering, versioning, and managing the lifecycle of AI agents within the platform. Provides metadata, documentation, and operational status for all registered agents.

Entry point and routing component that handles incoming requests and distributes them to appropriate AI agents based on capabilities, availability, and load balancing policies.

Kubernetes-native execution environment that manages the lifecycle, scaling, and resource allocation of AI agents. Provides automated deployment, scaling, and monitoring of agent instances.

Core platform layer containing the foundational components for AI agent orchestration, including AI Gateway, Agent Runtime, Connectors & Tools, and governance components. Built on Kubernetes infrastructure.

Component responsible for coordinating and orchestrating complex AI workflows across multiple agents and organizational boundaries. Manages task distribution, workflow execution, and inter-agent communication.

Application-level implementations of AI agent systems designed for specific enterprise use cases, such as Insurance Claims Processing, Healthcare Operations, or Procurement workflows.

Unified interface component that abstracts interactions with multiple Large Language Model (LLM) providers. Provides intelligent routing, failover capabilities, and consistent API interfaces across different AI service providers.

Security and safety component that implements content filtering, safety checks, access controls, and policy enforcement for AI interactions to ensure compliant and secure operations.

Comprehensive logging and tracking system that records all AI operations, decisions, and interactions for regulatory compliance, security monitoring, and operational transparency.

Deployment model that allows organizations to run the Agentic Layer components within their own cloud infrastructure, maintaining full control over data, security, and compliance requirements.

Design approach leveraging cloud computing principles, including containerization, microservices, dynamic orchestration, and declarative APIs. Built specifically for cloud environments with Kubernetes as the foundation.

Management interface providing real-time monitoring and reporting for regulatory compliance requirements including EU AI Act, GDPR, and industry-specific standards.

Integration components that enable seamless connectivity between the Agentic Layer and external systems, databases, APIs, and third-party services.

The Agentic Layer’s primary function as a management and orchestration system for AI-driven processes and autonomous agents within enterprise environments.

The concept of maintaining complete control and governance over data location, processing, and access within defined jurisdictional and organizational boundaries.

The foundational infrastructure layer providing container orchestration, scaling, resource management, and service discovery for all Agentic Layer components.

Architecture and components designed specifically to leverage Kubernetes primitives, APIs, and operational patterns for deployment, scaling, and management.

AI models trained on vast amounts of text data to understand and generate human-like text. Examples include GPT, Gemini, Claude, and other foundation models accessed through the AI Gateway.

Execution environment that supports agents built with different AI frameworks and technologies while providing consistent operational characteristics and management interfaces.

Operational interface providing comprehensive monitoring, metrics visualization, distributed tracing, and system health information for the entire Agentic Layer platform.

Integrated testing framework providing automated validation, performance testing, and quality assurance capabilities for AI agents before production deployment.

Dependency on a specific vendor’s proprietary technologies that makes switching to alternatives difficult or costly. The Agentic Layer’s open-source and agnostic design prevents vendor lock-in.